| $f$-Divergence Policy Optimization in Fully Decentralized Cooperative MARL |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| (Implicit) Ensembles of Ensembles: Epistemic Uncertainty Collapse in Large Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| 2SSP: A Two-Stage Framework for Structured Pruning of LLMs |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| A Baseline Method for Removing Invisible Image Watermarks using Deep Image Prior |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| A Bias Correction Mechanism for Distributed Asynchronous Optimization |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| A Case for Library-Level $k$-Means Binning in Histogram Gradient-Boosted Trees |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| A Comprehensive Survey of Contamination Detection Methods in Large Language Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| A Comprehensive Survey on Inverse Constrained Reinforcement Learning: Definitions, Progress and Challenges |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| A Comprehensive Survey on Knowledge Distillation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| A Curious Case of Remarkable Resilience to Gradient Attacks via Fully Convolutional and Differentiable Front End with a Skip Connection |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| A Framework for Finding Local Saddle Points in Two-Player Zero-Sum Black-Box Games |
✅ |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
4 |
| A Fused Gromov-Wasserstein Approach to Subgraph Contrastive Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| A Generalization Bound for Nearly-Linear Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| A Gold Standard Dataset for the Reviewer Assignment Problem |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| A Hierarchical Nearest Neighbour Approach to Contextual Bandits |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| A Lean Dataset for International Math Olympiad: Small Steps towards Writing Math Proofs for Hard Problems |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| A Learning-Based Framework for Fair and Scalable Solution Generation in Kidney Exchange Problems |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
2 |
| A Local Polyak-Łojasiewicz and Descent Lemma of Gradient Descent For Overparametrized Linear Models |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| A Max-Min Approach to the Worst-Case Class Separation Problem |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| A Mixture of Exemplars Approach for Efficient Out-of-Distribution Detection with Foundation Models |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| A Mutual Information Perspective on Multiple Latent Variable Generative Models for Positive View Generation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| A Neural Material Point Method for Particle-based Emulation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| A Note On The Stability Of The Focal Loss |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| A Note on Generalization in Variational Autoencoders: How Effective Is Synthetic Data and Overparameterization? |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| A Novel Benchmark for Few-Shot Semantic Segmentation in the Era of Foundation Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| A Pattern Language for Machine Learning Tasks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| A Practical Investigation of Spatially-Controlled Image Generation with Transformers |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| A Proximal Operator for Inducing 2:4-Sparsity |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| A Reproducibility Study of Decoupling Feature Extraction and Classification Layers for Calibrated Neural Networks |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| A Scalable Approach for Mapper via Efficient Spatial Search |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| A Self-Explainable Heterogeneous GNN for Relational Deep Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| A Shortcut-aware Video-QA Benchmark for Physical Understanding via Minimal Video Pairs |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| A Stochastic Polynomial Expansion for Uncertainty Propagation through Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| A Strong Baseline for Molecular Few-Shot Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| A Survey of Frontiers in LLM Reasoning: Inference Scaling, Learning to Reason, and Agentic Systems |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| A Survey of Recent Backdoor Attacks and Defenses in Large Language Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| A Survey of Reinforcement Learning from Human Feedback |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| A Survey of State Representation Learning for Deep Reinforcement Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| A Survey on Future Frame Synthesis: Bridging Deterministic and Generative Approaches |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| A Survey on Generative Modeling with Limited Data, Few Shots, and Zero Shot |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| A Survey on LLM Test-Time Compute via Search: Tasks, LLM Profiling, Search Algorithms, and Relevant Frameworks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| A Survey on Large Language Model Acceleration based on KV Cache Management |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| A Survey on Large Language Model-Based Social Agents in Game-Theoretic Scenarios |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| A Survey on Model MoErging: Recycling and Routing Among Specialized Experts for Collaborative Learning |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| A Survey on Verifiable Cross-Silo Federated Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| A Survey on the Honesty of Large Language Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| A Systematic Evaluation of the Planning and Scheduling Abilities of the Reasoning Model o1 |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| A Theoretical Study of Neural Network Expressive Power via Manifold Topology |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| A Unified Approach Towards Active Learning and Out-of-Distribution Detection |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| A Unified View of Double-Weighting for Marginal Distribution Shift |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| A User's Guide to Sampling Strategies for Sliced Optimal Transport |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| A Vector Bernstein Inequality for Self-Normalized Martingales |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| A comparison between humans and AI at recognizing objects in unusual poses |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| A functional framework for nonsmooth autodiff with {\it maxpooling} functions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| A general framework of Riemannian adaptive optimization methods with a convergence analysis |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| A limitation on black-box dynamics approaches to Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| A noise-corrected Langevin algorithm and sampling by half-denoising |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| A note on the $k$-means clustering for missing data |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| A reproducibility study of “User-item fairness tradeoffs in recommendations” |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| A second-order-like optimizer with adaptive gradient scaling for deep learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| A stochastic gradient descent algorithm with random search directions |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| A thorough reproduction and evaluation of $\mu$P |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| A unifying framework for generalised Bayesian online learning in non-stationary environments |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| AB-UPT: Scaling Neural CFD Surrogates for High- Fidelity Automotive Aerodynamics Simulations via Anchored- Branched Universal Physics Transformers |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| ABC: Achieving Better Control of Visual Embeddings using VLLMs |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| ADAPT to Robustify Prompt Tuning Vision Transformers |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| ADMIRE-BayesOpt: Accelerated Data MIxture RE-weighting for Language Models with Bayesian Optimization |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| AEAP: A Reinforcement Learning Actor Ensemble Algorithm with Adaptive Pruning |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| AI Agents That Matter |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| ALTA: Compiler-Based Analysis of Transformers |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| APR-CNN: Convolutional Neural Networks for the Adaptive Particle Representation of Large Microscopy Images |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| AQA-Bench: An Interactive Benchmark for Evaluating LLMs’ Sequential Reasoning Ability in Algorithmic Environments |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| ARVideo: Autoregressive Pretraining for Self-Supervised Video Representation Learning |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| ASTRA: A Scene-aware Transformer-based Model for Trajectory Prediction |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| ASkDAgger: Active Skill-level Data Aggregation for Interactive Imitation Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| AT4TS : Autotune for Time Series Foundation Models |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Abstraction for Bayesian Reinforcement Learning in Factored POMDPs |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| AcademicEval: Live Long-Context LLM Benchmark |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Accelerated Training on Low-Power Edge Devices |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Accelerating Learned Image Compression Through Modeling Neural Training Dynamics |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Accelerating Non-Conjugate Gaussian Processes By Trading Off Computation For Uncertainty |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Accounting for AI and Users Shaping One Another: The Role of Mathematical Models |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Accumulator-Aware Post-Training Quantization for Large Language Models |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| ActAlign: Zero-Shot Fine-Grained Video Classification via Language-Guided Sequence Alignment |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Activate and Adapt: A Two-Stage Framework for Open-Set Model Adaptation |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Activation sharding for scalable training of large models |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Active Diffusion Subsampling |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Active Learning via Classifier Impact and Greedy Selection for Interactive Image Retrieval |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Active Prompt Learning with Vision-Language Model Priors |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Adam-family Methods with Decoupled Weight Decay in Deep Learning |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Adapt then Unlearn: Exploring Parameter Space Semantics for Unlearning in Generative Adversarial Networks |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Adapting Chat Language Models Using Only Target Unlabeled Language Data |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Adaptive Clipping for Differential Private Federated Learning in Interpolation Regimes |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Adaptive Gradient Normalization and Independent Sampling for (Stochastic) Generalized-Smooth Optimization |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Adaptive Group Robust Ensemble Knowledge Distillation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Adaptive Incentive Design for Markov Decision Processes with Unknown Rewards |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Adaptive Mesh Quantization for Neural PDE Solvers |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Adaptive Multi-step Refinement Network for Robust Point Cloud Registration |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Adaptive Physics-informed Neural Networks: A Survey |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Adaptive Resolution Residual Networks — Generalizing Across Resolutions Easily and Efficiently |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Adjacency Search Embeddings |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Adversarial Bandits Against Arbitrary Strategies |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Adversarial Robustness of Graph Transformers |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Adversarial Subspace Generation for Outlier Detection in High-Dimensional Data |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Adversarial Surrogate Risk Bounds for Binary Classification |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Aggregating Algorithm and Axiomatic Loss Aggregation |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Agreement-Based Cascading for Efficient Inference |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| AlgoFormer: An Efficient Transformer Framework with Algorithmic Structures |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Algorithm Configuration for Structured Pfaffian Settings |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Algorithmic fairness with monotone likelihood ratios |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Align and Distill: Unifying and Improving Domain Adaptive Object Detection |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| AlignFix: Fixing Adversarial Perturbations by Agreement Checking for Adversarial Robustness against Black-box Attacks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Almost Sure Convergence of Stochastic Gradient Methods under Gradient Domination |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Alternators For Sequence Modeling |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Amdahl’s Law for LLMs: A Throughput-Centric Analysis of Extreme LLM Quantization |
❌ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
4 |
| Amortized Inference of Causal Models via Conditional Fixed-Point Iterations |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Amphibian: A Meta-Learning Framework for Rehearsal-Free, Fast Online Continual Learning |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| An Adversarial Perspective on Machine Unlearning for AI Safety |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| An Analysis of Model Robustness across Concurrent Distribution Shifts |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| An Analytical Model for Overparameterized Learning Under Class Imbalance |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
4 |
| An Architecture Built for Federated Learning: Addressing Data Heterogeneity through Adaptive Normalization-Free Feature Recalibration |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| An Asymptotically Optimal Algorithm for the Convex Hull Membership Problem |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| An Attribute-based Method for Video Anomaly Detection |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| An Efficient Sparse Fine-Tuning with Low Quantization Error via Neural Network Pruning |
❌ |
✅ |
✅ |
✅ |
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5 |
| An Efficient Training Algorithm for Models with Block-wise Sparsity |
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4 |
| An Embedding is Worth a Thousand Noisy Labels |
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5 |
| An Empirical Study of Pre-trained Model Selection for Out-of-Distribution Generalization and Calibration |
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6 |
| An Empirical Study of the Accuracy-Robustness Trade-off and Training Efficiency in Robust Self-Supervised Learning |
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6 |
| An Evolutionary Algorithm for Black-Box Adversarial Attack Against Explainable Methods |
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4 |
| An Expanded Benchmark that Rediscovers and Affirms the Edge of Uncertainty Sampling for Active Learning in Tabular Datasets |
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5 |
| An Information Theoretic Approach to Machine Unlearning |
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5 |
| An Information-Theoretic Lower Bound on the Generalization Error of Autoencoders |
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6 |
| An Unconditional Representation of the Conditional Score in Infinite Dimensional Linear Inverse Problems |
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4 |
| An analysis of the noise schedule for score-based generative models |
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5 |
| An elementary concentration bound for Gibbs measures arising in statistical learning theory |
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0 |
| Analysis of generalization capacities of Neural Ordinary Differential Equations |
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4 |
| Angular Regularization for Positive-Unlabeled Learning on the Hypersphere |
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3 |
| Any-Property-Conditional Molecule Generation with Self-Criticism using Spanning Trees |
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5 |
| Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs |
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3 |
| Approximation Rates and VC-Dimension Bounds for (P)ReLU MLP Mixture of Experts |
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5 |
| Approximation, Estimation and Optimization Errors for a Deep Neural Network |
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1 |
| Approximations to worst-case data dropping: unmasking failure modes |
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5 |
| Are Convex Optimization Curves Convex? |
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0 |
| Are Data Embeddings Effective in Time Series Forecasting? |
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6 |
| Are Domain Generalization Benchmarks with Accuracy on the Line Misspecified? |
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5 |
| Are Large Language Models Really Robust to Word-Level Perturbations? |
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3 |
| Are We Really Learning the Score Function? Reinterpreting Diffusion Models Through Wasserstein Gradient Flow Matching |
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3 |
| Ask Your Distribution Shift if Pre-Training is Right for You |
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5 |
| Associative memory inspires improvements for in-context learning using a novel attention residual stream architecture |
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4 |
| Assortment of Attention Heads: Accelerating Federated PEFT with Head Pruning and Strategic Client Selection |
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5 |
| Attention Mechanisms Don’t Learn Additive Models: Rethinking Feature Importance for Transformers |
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5 |
| Attention Overlap Is Responsible for The Entity Missing Problem in Text-to-image Diffusion Models! |
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3 |
| AttentionBreaker: Adaptive Evolutionary Optimization for Unmasking Vulnerabilities in LLMs through Bit-Flip Attacks |
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6 |
| AttentionSmithy: A Modular Framework for Rapid Transformer Development |
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5 |
| AttnGCG: Enhancing Jailbreaking Attacks on LLMs with Attention Manipulation |
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6 |
| AuToMATo: An Out-Of-The-Box Persistence-Based Clustering Algorithm |
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5 |
| Augmented Invertible Koopman Autoencoder for long-term time series forecasting |
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4 |
| Auto-Regressive vs Flow-Matching: a Comparative Study of Modeling Paradigms for Text-to-Music Generation |
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3 |
| AutoAnnotator: A Collaborative Annotation Framework for Large and Small Language Models |
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4 |
| AutoTrust: Benchmarking Trustworthiness in Large Vision Language Models for Autonomous Driving |
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5 |
| Automated Black-box Prompt Engineering for Personalized Text-to-Image Generation |
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5 |
| Autonomous Imagination: Closed-Loop Decomposition of Visual-to-Textual Conversion in Visual Reasoning for Multimodal Large Language Models |
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5 |
| Autoregressive Models in Vision: A Survey |
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1 |
| Avoiding Structural Pitfalls: Self-Supervised Low-Rank Feature Tuning for Graph Test-Time Adaptation |
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5 |
| B-cos LM: Efficiently Transforming Pre-trained Language Models for Improved Explainability |
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5 |
| BELLA: Black-box model Explanations by Local Linear Approximations |
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7 |
| BM$^2$: Coupled Schrödinger Bridge Matching |
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4 |
| Bags of Projected Nearest Neighbours: Competitors to Random Forests? |
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6 |
| Balanced Mixed-Type Tabular Data Synthesis with Diffusion Models |
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6 |
| Balancing Utility and Privacy: Dynamically Private SGD with Random Projection |
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5 |
| Batch Training for Streaming Time Series: A Transferable Augmentation Framework to Combat Distribution Shifts |
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3 |
| Batched Nonparametric Bandits via k-Nearest Neighbor UCB |
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3 |
| Bayesian Learning-driven Prototypical Contrastive Loss for Class-Incremental Learning |
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6 |
| Bayesian Neighborhood Adaptation for Graph Neural Networks |
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5 |
| Bayesian Optimization of Robustness Measures under Input Uncertainty: A Randomized Gaussian Process Upper Confidence Bound Approach |
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3 |
| Bayesian Transferability Assessment for Spiking Neural Networks |
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6 |
| Before Forgetting, There's Learning: Representation Learning Challenges in Online Unsupervised Continual Learning |
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6 |
| Behaviour Discovery and Attribution for Explainable Reinforcement Learning |
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4 |
| Between Linear and Sinusoidal: Rethinking the Time Encoder in Dynamic Graph Learning |
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4 |
| Beyond Grids: Multi-objective Bayesian Optimization With Adaptive Discretization |
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2 |
| Beyond Instance Consistency: Investigating View Diversity in Self-supervised Learning |
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5 |
| Beyond Joint Demonstrations: Personalized Expert Guidance for Efficient Multi-Agent Reinforcement Learning |
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3 |
| Beyond Marginals: Learning Joint Spatio-Temporal Patterns for Multivariate Anomaly Detection |
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5 |
| Beyond Parameter Count: Implicit Bias in Soft Mixture of Experts |
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5 |
| Beyond ordinary Lipschitz constraints: Differentially Private optimization with TNC |
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4 |
| Bi-Mamba: Towards Accurate 1-Bit State Space Model |
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4 |
| BiDoRA: Bi-level Optimization-Based Weight-Decomposed Low-Rank Adaptation |
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6 |
| Bigger is not Always Better: Scaling Properties of Latent Diffusion Models |
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4 |
| Blending adversarial training and representation-conditional purification via aggregation improves adversarial robustness |
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5 |
| Boosting Revisited: Benchmarking and Advancing LP-Based Ensemble Methods |
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7 |
| Bridging Causality, Individual Fairness, and Adversarial Robustness in the Absence of Structural Causal Model |
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3 |
| Bridging Lottery Ticket and Grokking: Understanding Grokking from Inner Structure of Networks |
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4 |
| Bridging the Training-Inference Gap in LLMs by Leveraging Self-Generated Tokens |
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5 |
| Budgeted-Bandits with Controlled Restarts with Applications in Learning and Computing |
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4 |
| Buffer-based Gradient Projection for Continual Federated Learning |
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6 |
| Building Blocks for Robust and Effective Semi-Supervised Real-World Object Detection |
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4 |
| Byzantine-Robust and Hessian-Free Federated Bilevel Optimization |
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3 |
| Bézier Flow: a Surface-wise Gradient Descent Method for Multi-objective Optimization |
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7 |
| CAREL: Instruction-guided reinforcement learning with cross-modal auxiliary objectives |
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5 |
| CLIP Meets Diffusion: A Synergistic Approach to Anomaly Detection |
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6 |
| CLImage: Human-Annotated Datasets for Complementary-Label Learning |
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6 |
| CLoQ: Enhancing Fine-Tuning of Quantized LLMs via Calibrated LoRA Initialization |
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6 |
| CNN Interpretability with Multivector Tucker Saliency Maps for Self-Supervised Models |
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5 |
| COMMA: A Communicative Multimodal Multi-Agent Benchmark |
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5 |
| COMPASS: COntinual Multilingual PEFT with Adaptive Semantic Sampling |
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5 |
| CREW-Wildfire: Benchmarking Agentic Multi-Agent Collaborations at Scale |
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5 |
| CXAD: Contrastive Explanations for Anomaly Detection: Algorithms, Complexity Results and Experiments |
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4 |
| CYCle: Choosing Your Collaborators Wisely to Enhance Collaborative Fairness in Decentralized Learning |
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6 |
| Calibrated Probabilistic Forecasts for Arbitrary Sequences |
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5 |
| Can AI-Generated Text be Reliably Detected? Stress Testing AI Text Detectors Under Various Attacks |
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4 |
| Can Kernel Methods Explain How the Data Affects Neural Collapse? |
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4 |
| Can Masked Autoencoders Also Listen to Birds? |
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6 |
| Can Optimization Trajectories Explain Multi-Task Transfer? |
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4 |
| Capsule Network Projectors are Equivariant and Invariant Learners |
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5 |
| Cardinality Sparsity: Applications in Matrix-Matrix Multiplications and Machine Learning |
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4 |
| Causal Discovery over High-Dimensional Structured Hypothesis Spaces with Causal Graph Partitioning |
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4 |
| Causal Dynamic Variational Autoencoder for Counterfactual Regression in Longitudinal Data |
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6 |
| Causal Ordering for Structure Learning from Time Series |
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4 |
| Celo: Training Versatile Learned Optimizers on a Compute Diet |
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6 |
| Certified Robustness to Data Poisoning in Gradient-Based Training |
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5 |
| Change Point Detection in Dynamic Graphs with Decoder-only Latent Space Model |
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6 |
| Change Point Detection in the Frequency Domain with Statistical Reliability |
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6 |
| Change Point Detection on A Separable Model for Dynamic Networks |
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5 |
| Characterizing Vision Backbones for Dense Prediction with Dense Attentive Probing |
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5 |
| Characterizing the Convergence of Game Dynamics via Potentialness |
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3 |
| Characterizing the Training Dynamics of Private Fine-tuning with Langevin diffusion |
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3 |
| Chimera: State Space Models Beyond Sequences |
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3 |
| Choose Your Model Size: Any Compression of Large Language Models Without Re-Computation |
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6 |
| Class Incremental Learning from First Principles: A Review |
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2 |
| Class-wise Generalization Error: an Information-Theoretic analysis |
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2 |
| Classifier-Free Guidance is a Predictor-Corrector |
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3 |
| Client-only Distributed Markov Chain Monte Carlo Sampling over a Network |
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2 |
| Closed-Form Diffusion Models |
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5 |
| Cluster Agnostic Network Lasso Bandits |
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3 |
| Cluster Tree for Nearest Neighbor Search |
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3 |
| Cluster and Predict Latents Patches for Improved Masked Image Modeling |
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6 |
| Clustering-Based Validation Splits for Model Selection under Domain Shift |
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5 |
| CoCoIns: Consistent Subject Generation via Contrastive Instantiated Concepts |
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4 |
| CoDe: Blockwise Control for Denoising Diffusion Models |
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5 |
| CoNNect: Connectivity-Based Regularization for Structural Pruning of Neural Networks |
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4 |
| CodeLutra: Boosting LLM Code Generation via Preference-Guided Refinement |
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4 |
| Collaboration with Dynamic Open Ad Hoc Team via Team State Modelling |
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5 |
| Collaborative Compressors in Distributed Mean Estimation with Limited Communication Budget |
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6 |
| ComFe: An Interpretable Head for Vision Transformers |
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6 |
| ComPEFT: Compression for Communicating Parameter Efficient Updates via Sparsification and Quantization |
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6 |
| Combating Inter-Task Confusion and Catastrophic Forgetting by Metric Learning and Re-Using a Past Trained Model |
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5 |
| Combinatorial Multi-armed Bandits: Arm Selection via Group Testing |
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❌ |
✅ |
❌ |
✅ |
✅ |
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5 |
| Combining Machine Learning Defenses without Conflicts |
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✅ |
❌ |
❌ |
✅ |
5 |
| Cometh: A continuous-time discrete-state graph diffusion model |
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❌ |
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6 |
| Commander-GPT: Dividing and Routing for Multimodal Sarcasm Detection |
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❌ |
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5 |
| Communication Cost Reduction for Subgraph Counting under Local Differential Privacy via Hash Functions |
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✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Communication-Efficient Heterogeneous Federated Learning with Generalized Heavy-Ball Momentum |
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✅ |
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✅ |
✅ |
7 |
| Comparing the information content of probabilistic representation spaces |
❌ |
✅ |
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❌ |
✅ |
❌ |
✅ |
4 |
| Complementarity: Toward Better Metrics and Optimizing Data Efficiency in LLMs |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
6 |
| Comprehension Without Competence: Architectural Limits of LLMs in Symbolic Computation and Reasoning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Compressed Decentralized Momentum Stochastic Gradient Methods for Nonconvex Optimization |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Concept Siever : Towards Controllable Erasure of Concepts from Diffusion Models without Side-effect |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Conditional Image Synthesis with Diffusion Models: A Survey |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Conditional Latent Space Molecular Scaffold Optimization for Accelerated Molecular Design |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Conformal Bounds on Full-Reference Image Quality for Imaging Inverse Problems |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Conformal Prediction: A Theoretical Note and Benchmarking Transductive Node Classification in Graphs |
✅ |
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✅ |
❌ |
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6 |
| Conformalized Credal Regions for Classification with Ambiguous Ground Truth |
✅ |
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✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Connecting Parameter Magnitudes and Hessian Eigenspaces at Scale using Sketched Methods |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Consistency Aware Robust Learning under Noisy Labels |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Consistency-Guided Asynchronous Contrastive Tuning for Few-Shot Class-Incremental Tuning of Foundation Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Constrained Reinforcement Learning with Smoothed Log Barrier Function |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Contextual Combinatorial Bandits With Changing Action Sets Via Gaussian Processes |
✅ |
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✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Continual Learning from Simulated Interactions via Multitask Prospective Rehearsal for Bionic Limb Behavior Modeling |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Continual Learning on CLIP via Incremental Prompt Tuning with Intrinsic Textual Anchors |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
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6 |
| Continual Pre-training of MoEs: How robust is your router? |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Continual learning via probabilistic exchangeable sequence modelling |
✅ |
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✅ |
✅ |
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❌ |
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6 |
| Continuous Language Model Interpolation yields Dynamic and Controllable Text Generation |
❌ |
✅ |
✅ |
✅ |
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❌ |
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5 |
| Continuous Parallel Relaxation for Finding Diverse Solutions in Combinatorial Optimization Problems |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Controlled Model Debiasing through Minimal and Interpretable Updates |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Controlling Statistical, Discretization, and Truncation Errors in Learning Fourier Linear Operators |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Convergence Aspects of Hybrid Kernel SVGD |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Convergence Guarantees for RMSProp and Adam in Generalized-smooth Non-convex Optimization with Affine Noise Variance |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Convergence Properties of Natural Gradient Descent for Minimizing KL Divergence |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Convergence of linear programming hierarchies for Gibbs states of spin systems |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Convex Relaxation for Solving Large-Margin Classifiers in Hyperbolic Space |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Cooperative Minibatching in Graph Neural Networks |
✅ |
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✅ |
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❌ |
✅ |
6 |
| Coreset-Driven Re-Labeling: Tackling Noisy Annotations with Noise-Free Gradients |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Coresets from Trajectories: Selecting Data via Correlation of Loss Differences |
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❌ |
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6 |
| Corner Cases: How Size and Position of Objects Challenge ImageNet-Trained Models |
❌ |
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❌ |
❌ |
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4 |
| Cost-Efficient Online Decision Making: A Combinatorial Multi-Armed Bandit Approach |
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❌ |
✅ |
❌ |
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5 |
| Counterfactual Fairness on Graphs: Augmentations, Hidden Confounders, and Identifiability |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| Counterfactual Learning of Stochastic Policies with Continuous Actions |
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❌ |
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6 |
| Counting Hours, Counting Losses: The Toll of Unpredictable Work Schedules on Financial Security |
✅ |
✅ |
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❌ |
❌ |
❌ |
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4 |
| Covariate-dependent Graphical Model Estimation via Neural Networks with Statistical Guarantees |
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❌ |
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6 |
| CroissantLLM: A Truly Bilingual French-English Language Model |
❌ |
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❌ |
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5 |
| Cross Entropy versus Label Smoothing: A Neural Collapse Perspective |
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❌ |
✅ |
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3 |
| Cross-Domain Graph Anomaly Detection via Test-Time Training with Homophily-Guided Self-Supervision |
❌ |
✅ |
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❌ |
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5 |
| Cross-lingual Transfer in Programming Languages: An Extensive Empirical Study |
❌ |
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6 |
| Ctrl-V: Higher Fidelity Autonomous Vehicle Video Generation with Bounding-Box Controlled Object Motion |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Cumulative Reasoning with Large Language Models |
✅ |
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❌ |
❌ |
❌ |
✅ |
4 |
| Curvature Diversity-Driven Deformation and Domain Alignment for Point Cloud |
❌ |
✅ |
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✅ |
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❌ |
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5 |
| Customizing Spider Silk: Generative Models with Mechanical Property Conditioning for Protein Engineering |
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✅ |
✅ |
✅ |
❌ |
❌ |
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4 |
| CyberThreat-Eval: Can Large Language Models Automate Real-World Threat Research? |
❌ |
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❌ |
❌ |
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4 |
| Cycle Conditioning for Robust Representation Learning from Categorical Data |
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❌ |
❌ |
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4 |
| D2 Actor Critic: Diffusion Actor Meets Distributional Critic |
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❌ |
❌ |
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4 |
| DA-DPO: Cost-efficient Difficulty-aware Preference Optimization for Reducing MLLM Hallucinations |
❌ |
❌ |
✅ |
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❌ |
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4 |
| DELTA: Dual Consistency Delving with Topological Uncertainty for Active Graph Domain Adaptation |
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7 |
| DIVINE: Diverse-Inconspicuous Feature Learning to Mitigate Abridge Learning |
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7 |
| DNOD: Deformable Neural Operators for Object Detection in SAR Images |
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✅ |
❌ |
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❌ |
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5 |
| DNR-Pruning: Sparsity-Aware Pruning via Dying Neuron Reactivation in Convolutional Neural Networks |
✅ |
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✅ |
❌ |
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❌ |
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5 |
| DP-2Stage: Adapting Language Models as Differentially Private Tabular Data Generators |
❌ |
✅ |
✅ |
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❌ |
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5 |
| DRAGON: Distributional Rewards Optimize Diffusion Generative Models |
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❌ |
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5 |
| DRDT3: Diffusion-Refined Decision Test-Time Training Model |
✅ |
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❌ |
✅ |
❌ |
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4 |
| DafnyBench: A Benchmark for Formal Software Verification |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
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6 |
| Daphne: Multi-Pass Compilation of Probabilistic Programs into Graphical Models and Neural Networks |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Data Augmentation Policy Search for Long-Term Forecasting |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Data Matters Most: Auditing Social Bias in Contrastive Vision–Language Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Data-Driven Discovery of PDEs via the Adjoint Method |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Dataset Condensation with Color Compensation |
✅ |
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✅ |
✅ |
✅ |
❌ |
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6 |
| Decentralized Projection-free Online Upper-Linearizable Optimization with Applications to DR-Submodular Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Decentralized Transformers with Centralized Aggregation are Sample-Efficient Multi-Agent World Models |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Decision-Focused Surrogate Modeling for Mixed-Integer Linear Optimization |
✅ |
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❌ |
✅ |
✅ |
✅ |
✅ |
6 |
| Decoding-based Regression |
❌ |
❌ |
✅ |
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✅ |
❌ |
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4 |
| Decomposed Direct Preference Optimization for Structure-Based Drug Design |
❌ |
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❌ |
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5 |
| Decomposing The Dark Matter of Sparse Autoencoders |
❌ |
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✅ |
✅ |
❌ |
❌ |
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4 |
| Decoupled Sequence and Structure Generation for Realistic Antibody Design |
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❌ |
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5 |
| Deep Active Learning in the Open World |
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6 |
| Deep Augmentation: Dropout as Augmentation for Self-Supervised Learning |
❌ |
❌ |
✅ |
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❌ |
❌ |
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3 |
| Deep Autoregressive Models as Causal Inference Engines |
❌ |
✅ |
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✅ |
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❌ |
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5 |
| Deep Koopman Learning using Noisy Data |
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❌ |
✅ |
✅ |
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❌ |
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5 |
| Deep Neural Networks and Brain Alignment: Brain Encoding and Decoding (Survey) |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| DeepRRTime: Robust Time-series Forecasting with a Regularized INR Basis |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Defending Against Unforeseen Failure Modes with Latent Adversarial Training |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Deflated Dynamics Value Iteration |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| DeformTime: capturing variable dependencies with deformable attention for time series forecasting |
❌ |
✅ |
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✅ |
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❌ |
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5 |
| Demystifying amortized causal discovery with transformers |
❌ |
✅ |
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✅ |
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❌ |
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5 |
| Denoising Pretrained Black-box Models via Amplitude-Guided Phase Realignment |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Density of states in neural networks: an in-depth exploration of learning in parameter space |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Dependency-Aware Semi-Structured Sparsity of GLU Variants in Large Language Models |
❌ |
✅ |
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✅ |
✅ |
❌ |
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5 |
| Dependency-aware Maximum Likelihood Estimation for Active Learning |
✅ |
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5 |
| Design Editing for Offline Model-based Optimization |
✅ |
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❌ |
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4 |
| Designing Algorithms Empowered by Language Models: An Analytical Framework, Case Studies, and Insights |
✅ |
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✅ |
❌ |
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4 |
| Designing a Conditional Prior Distribution for Flow-Based Generative Models |
✅ |
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❌ |
❌ |
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5 |
| Detecting Systematic Weaknesses in Vision Models along Predefined Human-Understandable Dimensions |
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✅ |
❌ |
❌ |
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5 |
| Dextr: Zero-Shot Neural Architecture Search with Singular Value Decomposition and Extrinsic Curvature |
❌ |
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❌ |
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5 |
| Diff-Instruct++: Training One-step Text-to-image Generator Model to Align with Human Preferences |
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❌ |
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6 |
| DiffCLIP: Differential Attention Meets CLIP |
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5 |
| DiffNat : Exploiting the Kurtosis Concentration Property for Image quality improvement |
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6 |
| DiffSampling: Enhancing Diversity and Accuracy in Neural Text Generation |
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7 |
| Differentiable Causal Discovery of Linear Non-Gaussian Acyclic Models Under Unmeasured Confounding |
✅ |
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✅ |
❌ |
❌ |
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4 |
| Differentially Private Clustered Federated Learning |
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✅ |
❌ |
❌ |
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4 |
| Differentially Private Gradient Flow based on the Sliced Wasserstein Distance |
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❌ |
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6 |
| Differentially Private Source-Target Clustering |
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❌ |
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6 |
| Differentiated Aggregation to Improve Generalization in Federated Learning |
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❌ |
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5 |
| Diffusion Model Predictive Control |
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4 |
| Diffusion Self-Weighted Guidance for Offline Reinforcement Learning |
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5 |
| Diffusion on Graph: Augmentation of Graph Structure for Node Classification |
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❌ |
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6 |
| Diffusion-RainbowPA: Improvements Integrated Preference Alignment for Diffusion-based Text-to-Image Generation |
❌ |
❌ |
✅ |
❌ |
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❌ |
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3 |
| Dimension reduction via score ratio matching |
✅ |
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❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Directed Exploration in Reinforcement Learning from Linear Temporal Logic |
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❌ |
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5 |
| Directed Graph Generation with Heat Kernels |
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4 |
| DisDet: Exploring Detectability of Backdoor Attack on Diffusion Models |
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5 |
| Disappearance of Timestep Embedding: A Case Study on Neural ODE and Diffusion Models |
❌ |
❌ |
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4 |
| Discovering group dynamics in coordinated time series via hierarchical recurrent switching-state models |
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5 |
| Discrete Audio Tokens: More Than a Survey! |
✅ |
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5 |
| Disentangled Embedding through Style and Mutual Information for Domain Generalization |
✅ |
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❌ |
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4 |
| Disentangled and Self-Explainable Node Representation Learning |
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5 |
| Disobeying Directions: Switching Random Walk Filters for Unsupervised Node Embedding Learning on Directed Graphs |
❌ |
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5 |
| Dissecting Bias in LLMs: A Mechanistic Interpretability Perspective |
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5 |
| Distilling Datasets Into Less Than One Image |
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5 |
| Distributed Hierarchical Decomposition Framework for Multimodal Timeseries Prediction |
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❌ |
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5 |
| Distributed Multi-Agent Lifelong Learning |
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❌ |
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❌ |
✅ |
4 |
| Distributed Quasi-Newton Method for Fair and Fast Federated Learning |
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❌ |
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6 |
| Distributed and Secure Kernel-Based Quantum Machine Learning |
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5 |
| Distributional Reduction: Unifying Dimensionality Reduction and Clustering with Gromov-Wasserstein |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Distributionally Robust Coreset Selection under Covariate Shift |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| DivIL: Unveiling and Addressing Over-Invariance for Out-of- Distribution Generalization |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Dive3D: Diverse Distillation-based Text-to-3D Generation via Score Implicit Matching |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Diverse Condensed Data Generation via Class Preserving Distribution Matching |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Diversify, Don't Fine-Tune: Scaling Up Visual Recognition Training with Synthetic Images |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Diversity Augmentation of Dynamic User Preference Data for Boosting Personalized Text Summarizers |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Diversity-Driven View Subset Selection for Indoor Novel View Synthesis |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Diversity-Enhanced and Classification-Aware Prompt Learning for Few-Shot Learning via Stable Diffusion |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Divide and Merge: Motion and Semantic Learning in End-to-End Autonomous Driving |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Do Concept Bottleneck Models Respect Localities? |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Do Think Tags Really Help LLMs Plan? A Critical Evaluation of ReAct-Style Prompting |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Does Unsupervised Domain Adaptation Improve the Robustness of Amortized Bayesian Inference? A Systematic Evaluation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Does confidence calibration improve conformal prediction? |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Does equivariance matter at scale? |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Domain Generalization for Time Series: Enhancing Drilling Regression Models for Stick-Slip Index Prediction |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Don’t Judge Before You CLIP: A Unified Approach for Perceptual Tasks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Double Horizon Model-Based Policy Optimization |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Double Machine Learning Based Structure Identification from Temporal Data |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Doubly Robust Conditional VAE via Decoder Calibration: An Implicit KL Annealing Approach |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Doubly Robust Uncertainty Quantification for Quantile Treatment Effects in Sequential Decision Making |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
4 |
| Downstream Task Guided Masking Learning in Masked Autoencoders Using Multi-Level Optimization |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Dual Caption Preference Optimization for Diffusion Models |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Dual Natural Gradient Descent for Scalable Training of Physics-Informed Neural Networks |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
✅ |
4 |
| DyGMamba: Efficiently Modeling Long-Term Temporal Dependency on Continuous-Time Dynamic Graphs with State Space Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Dynamic Pricing in the Linear Valuation Model using Shape Constraints |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Dynamic Schwartz-Fourier Neural Operator for Enhanced Expressive Power |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Dynamics of the accelerated t-SNE |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Dynamics-inspired Structure Hallucination for Protein-protein Interaction Modeling |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| EDM-TTS: Efficient Dual-Stage Masked Modeling for Alignment-Free Text-to-Speech Synthesis |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| EL-Clustering: Combining Upper- and Lower-Bounded Clusterings for Equitable Load Constraints |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| EMMA: Efficient Visual Alignment in Multi-Modal LLMs |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| EMMA: End-to-End Multimodal Model for Autonomous Driving |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| ETGL-DDPG: A Deep Deterministic Policy Gradient Algorithm for Sparse Reward Continuous Control |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Early Classification of Time Series: A Survey and Benchmark |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Early Directional Convergence in Deep Homogeneous Neural Networks for Small Initializations |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Effect of Random Learning Rate: Theoretical Analysis of SGD Dynamics in Non-Convex Optimization via Stationary Distribution |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Effective Backdoor Mitigation in Vision-Language Models Depends on the Pre-training Objective |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Efficient Diffusion Models: A Survey |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Efficient Distillation of Classifier-Free Guidance using Adapters |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Efficient Exploration in Multi-Agent Reinforcement Learning via Farsighted Self-Direction |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Efficient Few-Shot Continual Learning in Vision-Language Models |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Efficient Hardware Scaling and Diminishing Returns in Large-Scale Training of Language Models |
❌ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
4 |
| Efficient Knowledge Injection in LLMs via Self-Distillation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Efficient Multi-Agent Cooperation Learning through Teammate Lookahead |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Efficient Object-Centric Representation Learning using Masked Generative Modeling |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Efficient Open Set Single Image Test Time Adaptation of Vision Language Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Efficient Reasoning Models: A Survey |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Efficient Training of Multi-task Neural Solver for Combinatorial Optimization |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Efficient Vocabulary-Free Fine-Grained Visual Recognition in the Age of Multimodal LLMs |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Efficient and Accurate Optimal Transport with Mirror Descent and Conjugate Gradients |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Efficient and Flexible Neural Network Training through Layer-wise Feedback Propagation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Efficient and Unbiased Sampling from Boltzmann Distributions via Variance-Tuned Diffusion Models |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Efficient pooling of predictions via kernel embeddings |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Elucidating the Design Choice of Probability Paths in Flow Matching for Forecasting |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Emergent Corpus Pre-training Benefits Vision Language Models |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Emergent Neural Network Mechanisms for Generalization to Objects in Novel Orientations |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Emergent Semantics Beyond Token Embeddings: Transformer LMs with Frozen Visual Unicode Representations |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Emergent Symbol-like Number Variables in Artificial Neural Networks |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
5 |
| Emergent representations in networks trained with the Forward-Forward algorithm |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Empirical Bayes Trend Filtering Through a Variational Inference Framework |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Empirical Comparison of Membership Inference Attacks in Deep Transfer Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Enabling Automatic Differentiation with Mollified Graph Neural Operators |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Enabling Users to Falsify Deepfake Attacks |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Encoder-only Next Token Prediction |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| End-to-End Conformal Calibration for Optimization Under Uncertainty |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| End-to-end Training for Text-to-Image Synthesis using Dual-Text Embeddings |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Enhanced Federated Optimization: Adaptive Unbiased Client Sampling with Reduced Variance |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Enhancing Cost Efficiency in Active Learning with Candidate Set Query |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Enhancing Diversity in Text-to-Image Generation without Compromising Fidelity |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Enhancing Fairness in Unsupervised Graph Anomaly Detection through Disentanglement |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Enhancing Maritime Trajectory Forecasting via H3 Index and Causal Language Modelling (CLM) |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Enhancing Molecular Conformer Generation via Fragment- Augmented Diffusion Pretraining |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Enhancing Parameter Efficiency and Generalization in Large Models: A Regularized and Masked Low-Rank Adaptation Approach |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Enhancing Physics-Informed Neural Networks Through Feature Engineering |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
5 |
| Enhancing Plaque Segmentation in CCTA with Prompt- based Diffusion Data Augmentation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Enhancing Remaining Useful Life Prediction with Ensemble Multi-Term Fourier Graph Neural Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Enhancing Sample Generation of Diffusion Models using Noise Level Correction |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Enhancing deep neural networks through complex-valued representations and Kuramoto synchronization dynamics |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Ensemble Kalman Diffusion Guidance: A Derivative-free Method for Inverse Problems |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Ensemble and Mixture-of-Experts DeepONets For Operator Learning |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Entropy-Regularized Process Reward Model |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Equivalent Linear Mappings of Large Language Models |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Estimating the Event-Related Potential from Few EEG Trials |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Evaluating Compositional Scene Understanding in Multimodal Generative Models |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Evaluating Interpretable Methods via Geometric Alignment of Functional Distortions |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Evaluating Long Range Dependency Handling in Code Generation LLMs |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Evaluating Posterior Probabilities: Decision Theory, Proper Scoring Rules, and Calibration |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Evaluating explainability techniques on discrete-time graph neural networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Evaluating the Robustness of Analogical Reasoning in Large Language Models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Evaluation of Best-of-N Sampling Strategies for Language Model Alignment |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Event-Triggered Time-Varying Bayesian Optimization |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Evolution guided generative flow networks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Evolution of Discriminator and Generator Gradients in GAN Training: From Fitting to Collapse |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| ExCeL: Combined Extreme and Collective Logit Information for Out-of-Distribution Detection |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| ExDBN: Learning Dynamic Bayesian Networks using Extended Mixed-Integer Programming Formulations |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
1 |
| Exact Recovery Guarantees for Parameterized Nonlinear System Identification Problem under Sparse Disturbances or Semi-Oblivious Attacks |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
✅ |
2 |
| Expert Routing with Synthetic Data for Domain Incremental Learning |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Explaining Bayesian Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Explaining Caption-Image Interactions in CLIP Models with Second-Order Attributions |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Explaining Confident Black-Box Predictions |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Explaining Explainability: Recommendations for Effective Use of Concept Activation Vectors |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Explaining Node Embeddings |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Explaining the Behavior of Black-Box Prediction Algorithms with Causal Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Explanation Shift: How Did the Distribution Shift Impact the Model? |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Explicit Personalization and Local Training: Double Communication Acceleration in Federated Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Explicitly Disentangled Representations in Object-Centric Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Exploiting Benford's Law for Weight Regularization of Deep Neural Networks |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Exploring End-to-end Differentiable Neural Charged Particle Tracking – A Loss Landscape Perspective |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
5 |
| Exploring Weak-to-Strong Generalization for CLIP-based Classification |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Exploring exploration with foundation agents in interactive environments |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Exploring the Limitations of Layer Synchronization in Spiking Neural Networks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Exploring the Robustness of Language Models for Tabular Question Answering via Attention Analysis |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Exploring the potential of Direct Feedback Alignment for Continual Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Exponential Scaling of Factual Inconsistency in Data-to-Text Generation with Fine-Tuned LLMs |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Exponential tilting of subweibull distributions |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Expressive Pooling for Graph Neural Networks |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Expressiveness of Parametrized Distributions over DAGs for Causal Discovery |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Expressivity of Representation Learning on Continuous-Time Dynamic Graphs: An Information-Flow Centric Review |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Extending Graph Condensation to Multi-Label Datasets: A Benchmark Study |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| FB-MOAC: A Reinforcement Learning Algorithm for Forward-Backward Markov Decision Processes |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| FGAIF: Aligning Large Vision-Language Models with Fine-grained AI Feedback |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| FORTRESS: Fast, Tuning-Free Retrieval Ensemble for Scalable LLM Safety |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| FRAP: Faithful and Realistic Text-to-Image Generation with Adaptive Prompt Weighting |
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5 |
| FaAlGrad: Fairness through Alignment of Gradients across Different Subpopulations |
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✅ |
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5 |
| Factor Learning Portfolio Optimization Informed by Continuous-Time Finance Models |
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5 |
| Fair Online Influence Maximization |
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5 |
| Fair principal component analysis (PCA): minorization-maximization algorithms for Fair PCA, Fair Robust PCA and Fair Sparse PCA |
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3 |
| Fairness Through Matching |
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6 |
| Fairness and Disentanglement: A Critical Review of Predominant Bias in Neural Networks |
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3 |
| Fairness with respect to Stereotype Predictors: Impossibilities and Best Practices |
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3 |
| Fairness-Aware Dense Subgraph Discovery |
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6 |
| Faithful Interpretation for Graph Neural Networks |
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5 |
| Fast and Cost-effective Speculative Edge-Cloud Decoding with Early Exits |
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4 |
| Faster Diffusion Through Temporal Attention Decomposition |
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6 |
| FeatInv: Spatially resolved mapping from feature space to input space using conditional diffusion models |
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6 |
| FedComLoc: Communication-Efficient Distributed Training of Sparse and Quantized Models |
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5 |
| FedDUAL: A Dual-Strategy with Adaptive Loss and Dynamic Aggregation for Mitigating Data Heterogeneity in Federated Learning |
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5 |
| FedDr+: Stabilizing Dot-regression with Global Feature Distillation for Federated Learning |
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6 |
| FedHERO: A Federated Learning Approach for Node Classification Task on Heterophilic Graphs |
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6 |
| Federated Generalized Novel Category Discovery with Prompts Tuning |
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5 |
| Federated Learning on Virtual Heterogeneous Data with Local-Global Dataset Distillation |
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❌ |
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5 |
| Federated Learning with Efficient Local Adaptation for Realized Volatility Prediction |
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❌ |
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5 |
| Federated Learning with Uncertainty and Personalization via Efficient Second-order Optimization |
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4 |
| Federated Spectral Graph Transformers Meet Neural Ordinary Differential Equations for Non-IID Graphs |
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❌ |
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6 |
| Finetuning CLIP to Reason about Pairwise Differences |
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❌ |
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5 |
| FlashAttention on a Napkin: A Diagrammatic Approach to Deep Learning IO-Awareness |
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2 |
| Flexible Infinite-Width Graph Convolutional Neural Networks |
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5 |
| Flow map matching with stochastic interpolants: A mathematical framework for consistency models |
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3 |
| Flow-Attentional Graph Neural Networks |
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5 |
| FlowBench: Benchmarking Optical Flow Estimation Methods for Reliability and Generalization |
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❌ |
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5 |
| FlowKac: An Efficient Neural Fokker-Planck solver using Temporal Normalizing flows and the Feynman-Kac Formula |
✅ |
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❌ |
❌ |
✅ |
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4 |
| FoMo-0D: A Foundation Model for Zero-shot Tabular Outlier Detection |
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6 |
| FoldDiff: Folding in Point Cloud Diffusion |
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5 |
| Foldable SuperNets: Scalable Merging of Transformers with Different Initializations and Tasks |
❌ |
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❌ |
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4 |
| Forecasting Company Fundamentals |
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❌ |
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3 |
| Formal Verification of Graph Convolutional Networks with Uncertain Node Features and Uncertain Graph Structure |
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4 |
| Formulating Node Labelling as Node Classification or Link Prediction in Different Graph Representations |
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5 |
| Foundation Models Meet Federated Learning: A One-shot Feature-sharing Method with Privacy and Performance Guarantees |
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6 |
| Fourier Learning Machines: Nonharmonic Fourier-Based Neural Networks for Scientific Machine Learning |
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✅ |
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3 |
| Fourier PINNs: From Strong Boundary Conditions to Adaptive Fourier Bases |
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6 |
| FraGNNet: A Deep Probabilistic Model for Tandem Mass Spectrum Prediction |
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❌ |
6 |
| Fractal Generative Models |
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5 |
| FragFormer: A Fragment-based Representation Learning Framework for Molecular Property Prediction |
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5 |
| Frame-wise Conditioning Adaptation for Fine-Tuning Diffusion Models in Text-to-Video Prediction |
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5 |
| From Novelty to Imitation: Self-Distilled Rewards for Offline Reinforcement Learning |
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4 |
| From Promise to Practice: A Study of Common Pitfalls Behind the Generalization Gap in Machine Learning |
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5 |
| From Reasoning to Learning: A Survey on Hypothesis Discovery and Rule Learning with Large Language Models |
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0 |
| From Spikes to Heavy Tails: Unveiling the Spectral Evolution of Neural Networks |
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4 |
| Full-Rank Unsupervised Node Embeddings for Directed Graphs via Message Aggregation |
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6 |
| Fully Automatic Neural Network Reduction for Formal Verification |
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4 |
| FusionProt: Fusing Sequence and Structural Information for Unified Protein Representation Learning |
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6 |
| Future-aware Safe Active Learning of Time Varying Systems using Gaussian Processes |
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5 |
| G-RepsNet: A Lightweight Construction of Equivariant Networks for Arbitrary Matrix Groups |
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4 |
| G2D2: Gradient-Guided Discrete Diffusion for Inverse Problem Solving |
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6 |
| GLOV: Guided Large Language Models as Implicit Optimizers for Vision Language Models |
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6 |
| GMAgent: A Graph-oriented Multi-agent Collaboration Framework for Text-attributed Graph Analysis |
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5 |
| GRAPES: Learning to Sample Graphs for Scalable Graph Neural Networks |
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6 |
| GROOD: GRadient-Aware Out-of-Distribution Detection |
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5 |
| Gaussian Loss Smoothing Enables Certified Training with Tight Convex Relaxations |
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5 |
| Gaussian Pre-Activations in Neural Networks: Myth or Reality? |
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4 |
| Gaussian Processes with Bayesian Inference of Covariate Couplings |
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4 |
| Gaussian Scenes: Pose-Free Sparse-View Scene Reconstruction using Depth-Enhanced Diffusion Priors |
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7 |
| Gaussian mixture layers for neural networks |
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5 |
| GaussianFlow: Splatting Gaussian Dynamics for 4D Content Creation |
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4 |
| GeNIe: Generative Hard Negative Images Through Diffusion |
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6 |
| GenCAD: Image-Conditioned Computer-Aided Design Generation with Transformer-Based Contrastive Representation and Diffusion Priors |
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4 |
| GenOL: Generating Diverse Examples for Name-only Online Learning |
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6 |
| Generalizable Representation Learning for fMRI-based Neurological Disorder Identification |
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4 |
| Generalizable Spectral Embedding with an Application to UMAP |
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6 |
| Generalizable and Robust Spectral Method for Multi-view Representation Learning |
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6 |
| Generalized Compressed Sensing for Image Reconstruction with Diffusion Probabilistic Models |
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6 |
| Generalized Orders of Magnitude for Scalable, Parallel, High-Dynamic-Range Computation |
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4 |
| Generalized Out-of-Distribution Detection and Beyond in Vision Language Model Era: A Survey |
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4 |
| Generalized Prediction Set with Bandit Feedback |
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5 |
| Generalized Smooth Stochastic Variational Inequalities: Almost Sure Convergence and Convergence Rates |
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1 |
| Generalized Tangent Kernel: A Unified Geometric Foundation for Natural Gradient and Standard Gradient |
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6 |
| Generating Symbolic World Models via Test-time Scaling of Large Language Models |
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2 |
| Generative Feature Training of Thin 2-Layer Networks |
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6 |
| Generative Proto-Sequence: Sequence-Level Decision Making for Long-Horizon Reinforcement Learning |
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5 |
| Generative Risk Minimization for Out-of-Distribution Generalization on Graphs |
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6 |
| Genetic-Evolutionary Graph Neural Networks: A Paradigm for Improved Graph Representation Learning |
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4 |
| GeoMask3D: Geometrically Informed Mask Selection for Self-Supervised Point Cloud Learning in 3D |
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3 |
| Geometric Optimal Transport for Unsupervised Domain Adaptation |
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6 |
| Geometry-Aware visualization of high dimensional Symmetric Positive Definite matrices |
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5 |
| Getting aligned on representational alignment |
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1 |
| Global Convergence Rate of Deep Equilibrium Models with General Activations |
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2 |
| Global Graph Counterfactual Explanation: A Subgraph Mapping Approach |
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5 |
| Global Optimization Algorithm through High-Resolution Sampling |
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5 |
| Global Safe Sequential Learning via Efficient Knowledge Transfer |
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5 |
| Goal Recognition Design for General Behavioral Agents using Machine Learning |
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3 |
| Goal-Conditioned Data Augmentation for Offline Reinforcement Learning |
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4 |
| GradSkip: Communication-Accelerated Local Gradient Methods with Better Computational Complexity |
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3 |
| Gradient GA: Gradient Genetic Algorithm For Drug Molecular Design |
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4 |
| Gradient Inversion Attack on Graph Neural Networks |
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3 |
| Graph Fourier Neural ODEs: Modeling Spatial-temporal Multi-scales in Molecular Dynamics |
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5 |
| Graph Personalized Federated Learning via Client Network Learning |
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5 |
| Graph Theory-Based Deep Graph Similarity Learning: A Unified Survey of Pipeline, Techniques, and Challenges |
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1 |
| Graph-based Confidence Calibration for Large Language Models |
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3 |
| Graph-level Representation Learning with Joint-Embedding Predictive Architectures |
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5 |
| GraphFM: A generalist graph transformer that learns transferable representations across diverse domains |
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7 |
| Group Fair Federated Learning via Stochastic Kernel Regularization |
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5 |
| Group-robust Machine Unlearning |
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5 |
| Guided Discrete Diffusion for Electronic Health Record Generation |
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4 |
| HARE: Human-in-the-Loop Algorithmic Recourse |
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4 |
| HDCS: Hierarchy Discovery and Critic Shaping for Reinforcement Learning with Automaton Specification |
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3 |
| HalluEntity: Benchmarking and Understanding Entity-Level Hallucination Detection |
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3 |
| Hallucination Detection on a Budget: Efficient Bayesian Estimation of Semantic Entropy |
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5 |
| HandsOnVLM: Vision-Language Models for Hand-Object Interaction Prediction |
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5 |
| Hard Work Does Not Always Pay Off: On the Robustness of NAS to Data Poisoning |
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4 |
| Hard-Negative Prototype-Based Regularization for Few-Shot Class-Incremental Learning |
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4 |
| Hard-Negative Sampling for Contrastive Learning: Optimal Representation Geometry and Neural- vs Dimensional-Collapse |
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5 |
| Harmonic Loss Trains Interpretable AI Models |
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3 |
| Harmony: A Joint Self-Supervised and Weakly-Supervised Framework for Learning General Purpose Visual Representations |
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5 |
| Head-Specific Intervention Can Induce Misaligned AI Coordination in Large Language Models |
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5 |
| Heterogeneous Knowledge for Augmented Modular Reinforcement Learning |
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3 |
| Heterophily-informed Message Passing |
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5 |
| Hierarchical Language Model Design For Interpretable Graph Reasoning |
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4 |
| High-Dimensional Gaussian Process Regression with Soft Kernel Interpolation |
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5 |
| Higher Order Transformers With Kronecker-Structured Attention |
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5 |
| Highway Graph to Accelerate Reinforcement Learning |
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5 |
| Hitchhiker's guide on the relation of Energy-Based Models with other generative models, sampling and statistical physics: a comprehensive review |
✅ |
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❌ |
❌ |
❌ |
❌ |
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1 |
| HoSNNs: Adversarially-Robust Homeostatic Spiking Neural Networks with Adaptive Firing Thresholds |
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3 |
| Hodge-Aware Convolutional Learning on Simplicial Complexes |
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6 |
| HopCast: Calibration of Autoregressive Dynamics Models |
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5 |
| How Can Knowledge of a Task’s Modular Structure Improve Generalization and Training Efficiency? |
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4 |
| How Does Code Pretraining Affect Language Model Task Performance? |
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4 |
| How Many Images Does It Take? Estimating Imitation Thresholds in Text-to-Image Models |
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6 |
| How does overparametrization affect performance on minority groups? |
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4 |
| How far away are truly hyperparameter-free learning algorithms? |
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5 |
| How iteration composition influences convergence and stability in deep learning |
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✅ |
2 |
| How to Leverage Predictive Uncertainty Estimates for Reducing Catastrophic Forgetting in Online Continual Learning |
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❌ |
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4 |
| How to Upscale Neural Networks with Scaling Law? |
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0 |
| HyResPINNs: A Hybrid Residual Physics-Informed Neural Network Architecture Designed to Balance Expressiveness and Trainability |
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4 |
| HybridFlow: Quantification of Aleatoric and Epistemic Uncertainty with a Single Hybrid Model |
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4 |
| HyperMagNet: A Magnetic Laplacian based Hypergraph Neural Network |
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3 |
| HyperVQ: MLR-based Vector Quantization in Hyperbolic Space |
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5 |
| Hypergraph Neural Networks through the Lens of Message Passing: A Common Perspective to Homophily and Architecture Design |
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❌ |
✅ |
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3 |
| Hypergraphs as Weighted Directed Self-Looped Graphs: Spectral Properties, Clustering, Cheeger Inequality |
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❌ |
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4 |
| Hyperparameters in Continual Learning: A Reality Check |
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5 |
| I Want to Break Free! Persuasion and Anti-Social Behavior of LLMs in Multi-Agent Settings with Social Hierarchy |
❌ |
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❌ |
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3 |
| IPA: An Information-Reconstructive Input Projection Framework for Efficient Foundation Model Adaptation |
❌ |
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❌ |
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5 |
| Identification of Average Outcome under Interventions in Confounded Additive Noise Models |
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❌ |
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3 |
| Identifying Axiomatic Mathematical Transformation Steps using Tree-Structured Pointer Networks |
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❌ |
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4 |
| Identifying Macro Causal Effects in a C-DMG over ADMGs |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Identifying Spurious Correlations using Counterfactual Alignment |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Illusion or Algorithm? Investigating Memorization, Emergence, and Symbolic Processing in In-Context Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Illustrated Landmark Graphs for Long-horizon Policy Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Image and Video Quality Assessment using Prompt-Guided Latent Diffusion Models for Cross-Dataset Generalization |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
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6 |
| Implicit Bias and Fast Convergence Rates for Self-attention |
❌ |
❌ |
✅ |
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❌ |
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3 |
| Importance Weighting for Aligning Language Models under Deployment Distribution Shift |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Improved Localized Machine Unlearning Through the Lens of Memorization |
✅ |
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✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Improved seeding strategies for k-means and k-GMM |
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❌ |
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❌ |
✅ |
5 |
| Improving Adversarial Training for Two-player Competitive Games via Episodic Reward Engineering |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Improving CLIP Counting Accuracy via Parameter-Efficient Fine-Tuning |
❌ |
✅ |
✅ |
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❌ |
❌ |
✅ |
4 |
| Improving Consistency in Large Language Models through Chain of Guidance |
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❌ |
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6 |
| Improving GFlowNets for Text-to-Image Diffusion Alignment |
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❌ |
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❌ |
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5 |
| Improving Single-round Active Adaptation: A Prediction Variability Perspective |
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❌ |
❌ |
❌ |
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3 |
| In-context Learning for Mixture of Linear Regression: Existence, Generalization and Training Dynamics |
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❌ |
❌ |
❌ |
✅ |
2 |
| In-distribution adversarial attacks on object recognition models using gradient-free search. |
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✅ |
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❌ |
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6 |
| Incorporating Interventional Independence Improves Robustness against Interventional Distribution Shift |
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❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Incorporating Spatial Information into Goal-Conditioned Hierarchical Reinforcement Learning via Graph Representations |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Increasing Both Batch Size and Learning Rate Accelerates Stochastic Gradient Descent |
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❌ |
✅ |
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✅ |
6 |
| IndicFake Meets SAFARI-LLM: Unifying Semantic and Acoustic Intelligence for Multilingual Deepfake Detection |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
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3 |
| Influence Learning in Complex Systems |
❌ |
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✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Influential Bandits: Pulling an Arm May Change the Environment |
✅ |
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✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Information Theoretic Guarantees For Policy Alignment In Large Language Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Infrastructure for AI Agents |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Inherently Robust Control through Maximum-Entropy Learning-Based Rollout |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Initialization Matters: Unraveling the Impact of Pre-Training on Federated Learning |
❌ |
❌ |
✅ |
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❌ |
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4 |
| InkSight: Offline-to-Online Handwriting Conversion by Teaching Vision-Language Models to Read and Write |
❌ |
✅ |
✅ |
✅ |
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❌ |
✅ |
5 |
| Instance-Aware Graph Prompt Learning |
❌ |
✅ |
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❌ |
✅ |
5 |
| Integrating Large Language Models in Causal Discovery: A Statistical Causal Approach |
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✅ |
❌ |
❌ |
✅ |
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5 |
| Interactive Large Language Models for Reliable Answering under Incomplete Context |
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4 |
| Interactive Task Planning with Language Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Interpretable LLM-based Table Question Answering |
❌ |
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❌ |
❌ |
✅ |
4 |
| Interpreting Neurons in Deep Vision Networks with Language Models |
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✅ |
❌ |
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6 |
| Inverse Scaling in Test-Time Compute |
❌ |
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❌ |
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5 |
| Inverting Gradient Attacks Makes Powerful Data Poisoning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Investigating Continual Pretraining in Large Language Models: Insights and Implications |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| Investigating Generalization Behaviours of Generative Flow Networks |
❌ |
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❌ |
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5 |
| Investigating the Effects of Fairness Interventions Using Pointwise Representational Similarity |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Investigating the impact of missing value handling on Boosted trees and Deep learning for Tabular data: A Claim Reserving case study |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Is What You Ask For What You Get? Investigating Concept Associations in Text-to-Image Models |
❌ |
✅ |
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❌ |
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4 |
| Is Your LLM Secretly a World Model of the Internet? Model-Based Planning for Web Agents |
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❌ |
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6 |
| Is isotropy a good proxy for generalization in time series forecasting with transformers? |
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✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Iterated $Q$-Network: Beyond One-Step Bellman Updates in Deep Reinforcement Learning |
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❌ |
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5 |
| Jet: A Modern Transformer-Based Normalizing Flow |
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❌ |
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4 |
| Jigsaw-R1: A Study of Rule-based Visual Reinforcement Learning with Jigsaw Puzzles |
❌ |
✅ |
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✅ |
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❌ |
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5 |
| JoIN: Joint GANs Inversion for Intrinsic Image Decomposition |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
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5 |
| Joint Diffusion for Universal Hand-Object Grasp Generation |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Joint Generative Modeling of Grounded Scene Graphs and Images via Diffusion Models |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| KAGNNs: Kolmogorov-Arnold Networks meet Graph Learning |
❌ |
✅ |
✅ |
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❌ |
❌ |
✅ |
4 |
| Keep your distance: learning dispersed embeddings on $\mathbb{S}_{m}$ |
❌ |
✅ |
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✅ |
✅ |
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6 |
| Kernel Space Conditional Distribution Alignment for Improving Group Fairness in Deepfake Detection |
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❌ |
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4 |
| Knockout: A simple way to handle missing inputs |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Know Yourself and Know Your Neighbour : A Syntactically Informed Self-Supervised Compositional Sentence Representation Learning Framework using a Recursive Hypernetwork |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
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4 |
| Knowing What Not to Do: Leverage Language Model Insights for Action Space Pruning in Multi-agent Reinforcement Learning |
✅ |
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✅ |
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✅ |
✅ |
✅ |
7 |
| L2G: Repurposing Language Models for Genomics Tasks |
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❌ |
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6 |
| LAPP: Large Language Model Feedback for Preference-Driven Reinforcement Learning |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
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5 |
| LASE: Learned Adjacency Spectral Embeddings |
✅ |
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❌ |
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6 |
| LASP: Linear Attention Sequence Parallelism |
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❌ |
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6 |
| LBMamba: Locally Bi-directional Mamba |
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✅ |
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❌ |
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5 |
| LC-PLM: Long-context Protein Language Modeling Using Bidirectional Mamba with Shared Projection Layers |
✅ |
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7 |
| LCEN: A Nonlinear, Interpretable Feature Selection and Machine Learning Algorithm |
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✅ |
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❌ |
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5 |
| LEGO-Learn: Label-Efficient Graph Open-Set Learning |
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✅ |
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❌ |
❌ |
✅ |
5 |
| LIT-LVM: Structured Regularization for Interaction Terms in Linear Predictors using Latent Variable Models |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| LLM-Guided Self-Supervised Tabular Learning With Task-Specific Pre-text Tasks |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| LLM-Select: Feature Selection with Large Language Models |
❌ |
✅ |
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✅ |
❌ |
❌ |
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4 |
| LLM-TS Integrator: Integrating LLM for Enhanced Time Series Modeling |
✅ |
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✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| LLMs can learn self-restraint through iterative self-reflection |
✅ |
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✅ |
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❌ |
❌ |
✅ |
4 |
| LLaVA-OneVision: Easy Visual Task Transfer |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| LLaVA-Video: Video Instruction Tuning With Synthetic Data |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| LO-BCQ: Locally Optimal Block Clustered Quantization for 4-bit (W4A4) LLM Inference |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| LOGLO-FNO: Efficient Learning of Local and Global Features in Fourier Neural Operators |
✅ |
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✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| LTL-Constrained Policy Optimization with Cycle Experience Replay |
✅ |
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✅ |
❌ |
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4 |
| Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation |
✅ |
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✅ |
✅ |
✅ |
❌ |
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5 |
| Label Embedding via Low-Coherence Matrices |
✅ |
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✅ |
✅ |
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❌ |
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5 |
| Label Smoothing is a Pragmatic Information Bottleneck |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Labeling without Seeing? Blind Annotation for Privacy-Preserving Entity Resolution |
✅ |
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❌ |
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5 |
| LanPaint: Training-Free Diffusion Inpainting with Asymptotically Exact and Fast Conditional Sampling |
✅ |
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❌ |
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6 |
| Language Models Are Good Tabular Learners |
❌ |
✅ |
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✅ |
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❌ |
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5 |
| Language Models for Controllable DNA Sequence Design |
❌ |
✅ |
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✅ |
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❌ |
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5 |
| Language-assisted Feature Representation and Lightweight Active Learning For On-the-Fly Category Discovery |
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✅ |
✅ |
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❌ |
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5 |
| Large Action Models: From Inception to Implementation |
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✅ |
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❌ |
4 |
| Large Language Model Confidence Estimation via Black-Box Access |
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✅ |
✅ |
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❌ |
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5 |
| Large Language Model-Brained GUI Agents: A Survey |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Large-Scale Targeted Cause Discovery via Learning from Simulated Data |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Latent Adversarial Training Improves Robustness to Persistent Harmful Behaviors in LLMs |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Latent Covariate Shift: Unlocking Partial Identifiability for Multi-Source Domain Adaptation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Latent Space Energy-based Neural ODEs |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Latent Trajectory: A New Framework for Deep Actor-Critic Reinforcement Learning with Uncertainty Quantification |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Latent mixed-effect models for high-dimensional longitudinal data |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Latte: Latent Diffusion Transformer for Video Generation |
❌ |
❌ |
✅ |
❌ |
✅ |
✅ |
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4 |
| LeanProgress: Guiding Search for Neural Theorem Proving via Proof Progress Prediction |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
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4 |
| Learned-Database Systems Security |
❌ |
❌ |
✅ |
✅ |
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❌ |
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4 |
| Learning Actionable Counterfactual Explanations in Large State Spaces |
✅ |
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✅ |
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❌ |
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6 |
| Learning Deformable Body Interactions With Adaptive Spatial Tokenization |
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✅ |
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❌ |
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5 |
| Learning Energy-Based Generative Models via Potential Flow: A Variational Principle Approach to Probability Density Homotopy Matching |
✅ |
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✅ |
✅ |
❌ |
❌ |
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5 |
| Learning Equivalence Classes of Bayesian Network Structures with GFlowNet |
❌ |
❌ |
✅ |
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❌ |
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3 |
| Learning Federated Neural Graph Databases for Answering Complex Queries from Distributed Knowledge Graphs |
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❌ |
❌ |
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4 |
| Learning Is a Kan Extension |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Learning Linear Polytree Structural Equation Model |
✅ |
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❌ |
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6 |
| Learning Reward Machines from Partially Observed Policies |
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5 |
| Learning Robust Representations for Visual Reinforcement Learning via Task-Relevant Mask Sampling |
✅ |
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4 |
| Learning Task-Aware Abstract Representations for Meta-Reinforcement Learning |
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7 |
| Learning Time-Series Representations by Hierarchical Uniformity-Tolerance Latent Balancing |
✅ |
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7 |
| Learning Using a Single Forward Pass |
✅ |
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5 |
| Learning distributed representations with efficient SoftMax normalization |
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6 |
| Learning few-step posterior samplers by unfolding and distillation of diffusion models |
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5 |
| Learning in complex action spaces without policy gradients |
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❌ |
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❌ |
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4 |
| Learning the Language of Protein Structure |
✅ |
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❌ |
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6 |
| Learning to Be Cautious |
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❌ |
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6 |
| Learning to Prompt Your Domain for Federated Vision-Language Models |
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3 |
| Learning to Rank Features to Enhance Graph Neural Networks for Graph Classification |
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6 |
| Learning to Rank with Top-$K$ Fairness |
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6 |
| Length independent generalization bounds for deep SSM architectures via Rademacher contraction and stability constraints |
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0 |
| Leopard: A Vision Language Model for Text-Rich Multi- Image Tasks |
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4 |
| Let Your Light Shine: Foreground Portrait Matting via Deep Flash Priors |
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❌ |
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5 |
| Leveraging AutoML for Sustainable Deep Learning: A Multi- Objective HPO Approach on Deep Shift Neural Networks |
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❌ |
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6 |
| Leveraging Fully-Observable Solutions for Improved Partially-Observable Offline Reinforcement Learning |
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2 |
| Leveraging Gradients for Unsupervised Accuracy Estimation under Distribution Shift |
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❌ |
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✅ |
4 |
| Leveraging Unlabeled Data Sharing through Kernel Function Approximation in Offline Reinforcement Learning |
✅ |
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❌ |
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3 |
| Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE |
✅ |
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❌ |
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5 |
| Lie Symmetry Net: Preserving Conservation Laws in Modelling Financial Market Dynamics via Differential Equations |
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❌ |
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4 |
| Lifelong Learning in StyleGAN through Latent Subspaces |
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❌ |
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3 |
| LightTransfer: Your Long-Context LLM is Secretly a Hybrid Model with Effortless Adaptation |
✅ |
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❌ |
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❌ |
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4 |
| Linear Convergence of Decentralized FedAvg for PL Objectives: The Interpolation Regime |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Link Prediction with Relational Hypergraphs |
❌ |
✅ |
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✅ |
✅ |
❌ |
✅ |
5 |
| LitLLMs, LLMs for Literature Review: Are we there yet? |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Local Differential Privacy-Preserving Spectral Clustering for General Graphs |
✅ |
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✅ |
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❌ |
❌ |
✅ |
4 |
| Local Distribution-Based Adaptive Oversampling for Imbalanced Regression |
✅ |
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❌ |
❌ |
✅ |
5 |
| LocalFormer: Mitigating Over-Globalising in Transformers on Graphs with Localised Training |
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❌ |
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❌ |
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3 |
| Localize-and-Stitch: Efficient Model Merging via Sparse Task Arithmetic |
✅ |
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❌ |
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6 |
| Locret: Enhancing Eviction in Long-Context LLM Inference with Trained Retaining Heads on Consumer-Grade Devices |
✅ |
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❌ |
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4 |
| Long Context Transfer from Language to Vision |
❌ |
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✅ |
❌ |
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❌ |
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3 |
| Long Short-Term Imputer: Handling Consecutive Missing Values in Time Series |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| Long-Term Fairness Inquiries and Pursuits in Machine Learning: A Survey of Notions, Methods, and Challenges |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Long-context LLMs Struggle with Long In-context Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Loss Landscape Degeneracy and Stagewise Development in Transformers |
❌ |
✅ |
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❌ |
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5 |
| Loss-to-Loss Prediction: Scaling Laws for All Datasets |
❌ |
✅ |
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❌ |
❌ |
❌ |
✅ |
3 |
| Low Compute Unlearning via Sparse Representations |
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✅ |
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❌ |
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5 |
| Low-rank Momentum Factorization for Memory Efficient Training |
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❌ |
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6 |
| Lower Ricci Curvature for Efficient Community Detection |
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❌ |
❌ |
❌ |
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4 |
| LumiNet: Perception-Driven Knowledge Distillation via Statistical Logit Calibration |
❌ |
❌ |
✅ |
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4 |
| Lurie Networks with Robust Convergent Dynamics |
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✅ |
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❌ |
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5 |
| M3CoL: Harnessing Shared Relations via Multimodal Mixup Contrastive Learning for Multimodal Classification |
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✅ |
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❌ |
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5 |
| M4GN: Mesh-based Multi-segment Hierarchical Graph Network for Dynamic Simulations |
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❌ |
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5 |
| MACCA: Offline Multi-agent Reinforcement Learning with Causal Credit Assignment |
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❌ |
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❌ |
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4 |
| MAMUT: A Novel Framework for Modifying Mathematical Formulas for the Generation of Specialized Datasets for Language Model Training |
✅ |
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✅ |
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✅ |
✅ |
7 |
| MDTree: A Masked Dynamic Autoregressive Model for Phylogenetic Inference |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| MESSI: A Multi-Elevation Semantic Segmentation Image Dataset of an Urban Environment |
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❌ |
✅ |
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5 |
| MGPATH: A Vision-Language Model with Multi-Granular Prompt Learning for Few-Shot Whole Slide Pathology Classification |
❌ |
✅ |
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❌ |
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5 |
| MIND: Modality-Informed Knowledge Distillation Framework for Multimodal Clinical Prediction Tasks |
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❌ |
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6 |
| MMD Two-sample Testing in the Presence of Arbitrarily Missing Data |
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3 |
| MOCK: an Algorithm for Learning Nonparametric Differential Equations via Multivariate Occupation Kernel Functions |
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7 |
| MOORL: A Framework for Integrating Offline-Online Reinforcement Learning |
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5 |
| MUC: Machine Unlearning for Contrastive Learning with Black-box Evaluation |
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5 |
| Machine Learning with Physics Knowledge for Prediction: A Survey |
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❌ |
❌ |
❌ |
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1 |
| MagicPose4D: Crafting Articulated Models with Appearance and Motion Control |
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3 |
| Making Reliable and Flexible Decisions in Long-tailed Classification |
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❌ |
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6 |
| Making Self-supervised Learning Robust to Spurious Correlation via Learning-speed Aware Sampling |
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5 |
| Mamba State-Space Models Are Lyapunov-Stable Learners |
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4 |
| MarDini: Masked Auto-regressive Diffusion for Video Generation at Scale |
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3 |
| MaskRIS: Semantic Distortion-aware Data Augmentation for Referring Image Segmentation |
❌ |
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❌ |
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4 |
| Masked Capsule Autoencoders |
❌ |
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✅ |
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❌ |
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3 |
| Mastering SAM Prompts: A Large-Scale Empirical Study in Segmentation Refinement for Scientific Imaging |
✅ |
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❌ |
❌ |
❌ |
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4 |
| Mathematical Characterization of Better-than-Random Multiclass Models |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| MaxCutBench: Revisiting and Benchmarking Graph Neural Networks for Maximum Cut |
✅ |
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7 |
| Maximally Expressive GNNs for Outerplanar Graphs |
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❌ |
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6 |
| Maximising the Utility of Validation Sets for Imbalanced Noisy-label Meta-learning |
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5 |
| Maximum Mean Discrepancy on Exponential Windows for Online Change Detection |
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5 |
| Maxwell's Demon at Work: Efficient Pruning by Leveraging Saturation of Neurons |
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❌ |
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6 |
| Mean-Field RL for Large-Scale Unit-Capacity Pickup-and-Delivery Problems |
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❌ |
❌ |
✅ |
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4 |
| Measuring Data Science Automation: A Survey of Evaluation Tools for AI Assistants and Agents |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Melody or Machine: Detecting Synthetic Music with Dual-Stream Contrastive Learning |
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❌ |
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6 |
| MemBench: Memorized Image Trigger Prompt Dataset for Diffusion Models |
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❌ |
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6 |
| MemLLM: Finetuning LLMs to Use Explicit Read-Write Memory |
✅ |
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✅ |
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❌ |
❌ |
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5 |
| MemeSense: An Adaptive In-Context Framework for Social Commonsense Driven Meme Moderation |
❌ |
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❌ |
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4 |
| Memory-Modular Classification: Learning to Generalize with Memory Replacement |
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4 |
| Mental Modelling of Reinforcement Learning Agents by Language Models |
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❌ |
❌ |
❌ |
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2 |
| Mesh-Informed Neural Operator : A Transformer Generative Approach |
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❌ |
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5 |
| Meta-Learning Adaptive Loss Functions |
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❌ |
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6 |
| Meta-Learning for Graphs with Heterogeneous Node Attribute Spaces for Few-Shot Edge Predictions |
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6 |
| Meta-Learning to Teach Semantic Prompts for Open Domain Generalization in Vision-Language Models |
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❌ |
❌ |
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4 |
| Meta-learning Optimizers for Communication-Efficient Learning |
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4 |
| Meta-learning Population-based Methods for Reinforcement Learning |
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5 |
| MetaGFN: Exploring Distant Modes with Adapted Metadynamics for Continuous GFlowNets |
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❌ |
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4 |
| Metalearning Continual Learning Algorithms |
❌ |
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5 |
| Metamorphic Forward Adaptation Network: Dynamically Adaptive and Modular Multi-layer Learning |
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4 |
| Min-Max Optimisation for Nonconvex-Nonconcave Functions Using a Random Zeroth-Order Extragradient Algorithm |
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❌ |
❌ |
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4 |
| Mind the Confidence Gap: Overconfidence, Calibration, and Distractor Effects in Large Language Models |
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❌ |
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4 |
| MiniFold: Simple, Fast, and Accurate Protein Structure Prediction |
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❌ |
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6 |
| Minimax Lower Bounds for Estimating Distributions on Low-dimensional Spaces |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Minimax Multi-Target Conformal Prediction with Applications to Imaging Inverse Problems |
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✅ |
✅ |
❌ |
❌ |
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4 |
| Minimax Posterior Contraction Rates for Unconstrained Distribution Estimation on $[0,1]^d$ under Wasserstein Distance |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
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0 |
| Mirror Descent Policy Optimisation for Robust Constrained Markov Decision Processes |
✅ |
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✅ |
❌ |
❌ |
❌ |
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4 |
| Mixed Sparsity Training: Achieving 4$\times$ FLOP Reduction for Transformer Pretraining |
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7 |
| Mixed-View Panorama Synthesis using Geospatially Guided Diffusion |
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❌ |
✅ |
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❌ |
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4 |
| Mixture Degree-Corrected Stochastic Block Model for Multi-Group Community Detection in Multiplex Graphs |
✅ |
❌ |
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❌ |
❌ |
❌ |
❌ |
2 |
| Mixture of Balanced Information Bottlenecks for Long-Tailed Visual Recognition |
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✅ |
❌ |
❌ |
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5 |
| Mixture of Cache-Conditional Experts for Efficient Mobile Device Inference |
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4 |
| Mixture of Experts for Image Classification: What's the Sweet Spot? |
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❌ |
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4 |
| Mixture-of-Transformers: A Sparse and Scalable Architecture for Multi-Modal Foundation Models |
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5 |
| MoFO: Momentum-Filtered Optimizer for Mitigating Forgetting in LLM Fine-Tuning |
✅ |
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❌ |
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6 |
| MoReact: Generating Reactive Motion from Textual Descriptions |
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❌ |
✅ |
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❌ |
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4 |
| MobileCLIP2: Improving Multi-Modal Reinforced Training |
❌ |
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❌ |
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5 |
| Model Guidance via Robust Feature Attribution |
❌ |
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✅ |
❌ |
❌ |
❌ |
3 |
| Model Tampering Attacks Enable More Rigorous Evaluations of LLM Capabilities |
❌ |
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❌ |
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4 |
| Model Tensor Planning |
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4 |
| Model-free reinforcement learning with noisy actions for automated experimental control in optics |
❌ |
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❌ |
❌ |
✅ |
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4 |
| Modeling Human Beliefs about AI Behavior for Scalable Oversight |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| ModernTCN Revisited: A Critical Look at the Experimental Setup in General Time Series Analysis |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Modularity aided consistent attributed graph clustering via coarsening |
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❌ |
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6 |
| Monocular Dynamic Gaussian Splatting: Fast, Brittle, and Scene Complexity Rules |
❌ |
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❌ |
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5 |
| Monotone Missing Data: A Blessing and a Curse |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Multi-Attribute Constraint Satisfaction via Language Model Rewriting |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Multi-BK-Net: Multi-Branch Multi-Kernel Convolutional Neural Networks for Clinical EEG Analysis |
❌ |
✅ |
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✅ |
❌ |
❌ |
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4 |
| Multi-Bellman operator for convergence of $Q$-learning with linear function approximation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
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3 |
| Multi-Modal Foundation Models for Computational Pathology: A Survey |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Multi-Output Distributional Fairness via Post-Processing |
✅ |
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✅ |
❌ |
❌ |
✅ |
5 |
| Multi-model Online Conformal Prediction with Graph-Structured Feedback |
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✅ |
✅ |
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❌ |
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5 |
| Multi-objective Bayesian optimization for Likelihood-Free inference in sequential sampling models of decision making |
✅ |
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✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Multimodal Cultural Safety: Evaluation Framework and Alignment Strategies |
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❌ |
✅ |
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✅ |
❌ |
✅ |
3 |
| Multiplayer Information Asymmetric Contextual Bandits |
✅ |
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❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Multivariate Dense Retrieval: A Reproducibility Study under a Memory-limited Setup |
❌ |
✅ |
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❌ |
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5 |
| Music Foundation Model as Generic Booster for Music Downstream Tasks |
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❌ |
✅ |
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4 |
| NITO: Neural Implicit Fields for Resolution-free and Domain-Adaptable Topology Optimization |
❌ |
✅ |
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❌ |
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5 |
| Necessary and Sufficient Watermark for Large Language Models |
✅ |
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✅ |
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❌ |
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5 |
| NeedleBench: Evaluating LLM Retrieval and Reasoning Across Varying Information Densities |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| NeoBERT: A Next Generation BERT |
❌ |
✅ |
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✅ |
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❌ |
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5 |
| NeurIPS 2023 Competition: Privacy Preserving Federated Learning Document VQA |
✅ |
✅ |
✅ |
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❌ |
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6 |
| Neural Deconstruction Search for Vehicle Routing Problems |
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7 |
| Neural Lattice Reduction: A Self-Supervised Geometric Deep Learning Approach |
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❌ |
✅ |
❌ |
✅ |
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4 |
| Neural ODE and SDE Models for Adaptation and Planning in Model-Based Reinforcement Learning |
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5 |
| Neural Slot Interpreters: Grounding Object Semantics in Emergent Slot Representations |
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5 |
| Neural Spatiotemporal Point Processes: Trends and Challenges |
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1 |
| Neural varifolds: an aggregate representation for quantifying the geometry of point clouds |
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6 |
| Neuron-based explanations of neural networks sacrifice completeness and interpretability |
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4 |
| No $D_{train}$: Model-Agnostic Counterfactual Explanations Using Reinforcement Learning |
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4 |
| No Detail Left Behind: Revisiting Self-Retrieval for Fine-Grained Image Captioning |
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5 |
| No Need for Ad-hoc Substitutes: The Expected Cost is a Principled All-purpose Classification Metric |
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4 |
| Node Classification With Reject Option |
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3 |
| Node Duplication Improves Cold-start Link Prediction |
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7 |
| Node Embeddings via Neighbor Embeddings |
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5 |
| Node Feature Forecasting in Temporal Graphs: an Interpretable Online Algorithm |
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5 |
| Node-Level Data Valuation on Graphs |
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5 |
| Noise-free Loss Gradients: A Surprisingly Effective Baseline for Coreset Selection |
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6 |
| Nomic Embed: Training a Reproducible Long Context Text Embedder |
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4 |
| Non asymptotic analysis of Adaptive stochastic gradient algorithms and applications |
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1 |
| Non-Myopic Multi-Objective Bayesian Optimization |
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5 |
| Normality-Guided Distributional Reinforcement Learning for Continuous Control |
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4 |
| Numerically Robust Fixed-Point Smoothing Without State Augmentation |
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4 |
| ODEStream: A Buffer-Free Online Learning Framework with ODE-based Adaptor for Streaming Time Series Forecasting |
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5 |
| ODNet: Opinion Dynamics-Inspired Neural Message Passing for Graphs and Hypergraphs |
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5 |
| Oblique Bayesian Additive Regression Trees |
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5 |
| Occam’s Razor for SSL: Memory-Efficient Parametric Instance Discrimination |
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5 |
| Offline Learning and Forgetting for Reasoning with Large Language Models |
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6 |
| Offset Unlearning for Large Language Models |
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3 |
| OmniInput: An Evaluation Framework for Deep Learning Models on Internet-Scale Data |
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3 |
| On Convolutions, Intrinsic Dimension, and Diffusion Models |
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0 |
| On Efficient Bayesian Exploration in Model-Based Reinforcement Learning |
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3 |
| On Inherent Adversarial Robustness of Active Vision Systems |
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4 |
| On Joint Regularization and Calibration in Deep Ensembles |
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5 |
| On Memorization in Diffusion Models |
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4 |
| On Representing Convex Quadratically Constrained Quadratic Programs via Graph Neural Networks |
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5 |
| On Space Folds of ReLU Neural Networks |
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4 |
| On Sparsity and Sub-Gaussianity in the Johnson- Lindenstrauss Lemma |
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0 |
| On The Landscape of Spoken Language Models: A Comprehensive Survey |
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0 |
| On Time Series Clustering with Graph Neural Networks |
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5 |
| On Training-Conditional Conformal Prediction and Binomial Proportion Confidence Intervals |
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2 |
| On Using Certified Training towards Empirical Robustness |
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5 |
| On Using Secure Aggregation in Differentially Private Federated Learning with Multiple Local Steps |
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6 |
| On diffusion posterior sampling via sequential Monte Carlo for zero-shot scaffolding of protein motifs |
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5 |
| On diffusion-based generative models and their error bounds: The log-concave case with full convergence estimates |
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1 |
| On the Challenges and Opportunities in Generative AI |
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1 |
| On the Convergence Rates of Federated Q-Learning across Heterogeneous Environments |
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2 |
| On the Convergence of SVGD in KL divergence via Approximate gradient flow |
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4 |
| On the Detection of Reviewer-Author Collusion Rings From Paper Bidding |
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4 |
| On the Expressiveness of Softmax Attention: A Recurrent Neural Network Perspective |
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5 |
| On the Generalizability of "Competition of Mechanisms: Tracing How Language Models Handle Facts and Counterfactuals" |
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4 |
| On the Hardness of Computing Counterfactual and Semi-factual Explanations in XAI |
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0 |
| On the Low-Rank Parametrization of Reward Models for Controlled Language Generation |
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4 |
| On the Problem of Consistent Anomalies in Zero-Shot Industrial Anomaly Detection |
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6 |
| On the Properties and Estimation of Pointwise Mutual Information Profiles |
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3 |
| On the Regularization of Learnable Embeddings for Time Series Forecasting |
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5 |
| On the Robustness of Kolmogorov-Arnold Networks: An Adversarial Perspective |
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3 |
| On the Role of Discrete Representation in Sparse Mixture of Experts |
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5 |
| On the Sample Complexity of One Hidden Layer Networks with Equivariance, Locality and Weight Sharing |
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4 |
| On the Utility of Existing Fine-Tuned Models on Data-Scarce Domains |
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4 |
| On the effectiveness of Rotation-Equivariance in U-Net: A Benchmark for Image Segmentation |
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5 |
| On the effects of similarity metrics in decentralized deep learning under distribution shift |
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4 |
| On the stability of gradient descent with second order dynamics for time-varying cost functions |
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2 |
| One-Shot Federated Distillation Using Monoclass Teachers: A Study of Knowledge Fragmentation and Out-of-Distribution Supervision |
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3 |
| Online Bandit Nonlinear Control with Dynamic Batch Length and Adaptive Learning Rate |
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3 |
| Online Control-Informed Learning |
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3 |
| Online Selective Conformal Inference: Errors and Solutions |
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2 |
| Open Problems in Mechanistic Interpretability |
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0 |
| Open Problems in Technical AI Governance |
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0 |
| Operationalizing a Threat Model for Red-Teaming Large Language Models (LLMs) |
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1 |
| Optimal Embedding Guided Negative Sample Generation for Knowledge Graph Link Prediction |
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5 |
| Optimal Transport for Domain Adaptation through Gaussian Mixture Models |
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4 |
| Optimization Dynamics of Equivariant and Augmented Neural Networks |
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5 |
| Optimization Guarantees for Square-Root Natural-Gradient Variational Inference |
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5 |
| Optimization and Generalization Guarantees for Weight Normalization |
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4 |
| Optimizing Cycle Life Prediction of Lithium-ion Batteries via a Physics-Informed Model |
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4 |
| Optimizing Estimators of Squared Calibration Errors in Classification |
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6 |
| Optimizing Time Series Forecasting Architectures: A Hierarchical Neural Architecture Search Approach |
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5 |
| Oscillations Make Neural Networks Robust to Quantization |
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4 |
| Out of Spuriousity: Improving Robustness to Spurious Correlations without Group Annotations |
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4 |
| Out-of-Distribution Learning with Human Feedback |
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7 |
| Outcome-based Reinforcement Learning to Predict the Future |
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4 |
| Over-parameterised Shallow Neural Networks with Asymmetrical Node Scaling: Global Convergence Guarantees and Feature Learning |
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4 |
| Overcoming Knowledge Barriers: Online Imitation Learning from Visual Observation with Pretrained World Models |
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6 |
| Overcoming Non-stationary Dynamics with Evidential Proximal Policy Optimization |
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5 |
| PASCAL: Precise and Efficient ANN- SNN Conversion using Spike Accumulation and Adaptive Layerwise Activation |
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6 |
| PCF Learned Sort: a Learning Augmented Sort Algorithm with $\mathcal{O}(n \log\log n)$ Expected Complexity |
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5 |
| PICore: Physics-Informed Unsupervised Coreset Selection for Data Efficient Neural Operator Training |
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4 |
| PRIMO: Private Regression in Multiple Outcomes |
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3 |
| PROPS: Progressively Private Self-alignment of Large Language Models |
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5 |
| PROXI: Challenging the GNNs for Link Prediction |
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5 |
| PSC: Posterior Sampling-Based Compression |
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5 |
| Part-aware Prompted Segment Anything Model for Adaptive Segmentation |
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5 |
| PartSDF: Part-Based Implicit Neural Representation for Composite 3D Shape Parametrization and Optimization |
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5 |
| Partial-Label Learning with a Reject Option |
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5 |
| Partially Frozen Random Networks Contain Compact Strong Lottery Tickets |
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4 |
| Partially Personalized Federated Learning: Breaking the Curse of Data Heterogeneity |
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5 |
| Path-Specific Counterfactual Fairness via Dividend Correction |
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5 |
| Permissive Information-Flow Analysis for Large Language Models |
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4 |
| Personalization of Large Language Models: A Survey |
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1 |
| Personalized Federated Learning of Probabilistic Models: A PAC-Bayesian Approach |
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5 |
| Personalized Federated Learning via Low-Rank Matrix Optimization |
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5 |
| Personalized Layer Selection for Graph Neural Networks |
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5 |
| Personalized Negative Reservoir for Incremental Learning in Recommender Systems |
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5 |
| Personalized Privacy Amplification via Importance Sampling |
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5 |
| PersonalizedRouter: Personalized LLM Routing via Graph-based User Preference Modeling |
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5 |
| Phase-driven Generalizable Representation Learning for Nonstationary Time Series Classification |
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5 |
| Physics of Language Models: Part 1, Learning Hierarchical Language Structures |
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5 |
| Physics-Aware Spatiotemporal Causal Graph Network for Forecasting with Limited Data |
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4 |
| Piecewise Constant Spectral Graph Neural Network |
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6 |
| Pitfalls in Evaluating Inference-time Methods for Improving LLM Reliability |
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4 |
| PixelWorld: Towards Perceiving Everything as Pixels |
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5 |
| Policy Optimization via Adv2: Adversarial Learning on Advantage Functions |
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1 |
| Policy-Guided Search on Tree-of-Thoughts for Efficient Problem Solving with Bounded Language Model Queries |
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4 |
| Positional Encoder Graph Quantile Neural Networks for Geographic Data |
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6 |
| Posterior Sampling for Reinforcement Learning on Graphs |
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1 |
| Potential Score Matching: Debiasing Molecular Structure Sampling with Potential Energy Guidance |
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5 |
| Pre-Training Representations of Binary Code Using Contrastive Learning |
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6 |
| Pre-trained Language Models Improve the Few-shot Prompt Ability of Decision Transformer |
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6 |
| Pre-trained Vision-Language Models Learn Discoverable Visual Concepts |
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5 |
| Predictable Reinforcement Learning Dynamics through Entropy Rate Minimization |
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3 |
| Predicting sub-population specific viral evolution |
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5 |
| Predictive Control and Regret Analysis of Non-Stationary MDP with Look-ahead Information |
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2 |
| Pref-GUIDE: Continual Policy Learning from Real-Time Human Feedback via Preference-Based Learning |
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4 |
| Preference Discerning with LLM-Enhanced Generative Retrieval |
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7 |
| Preferential Multi-Objective Bayesian Optimization |
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4 |
| Preserving Angles Improves Feature Distillation |
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6 |
| Preserving Expert-Level Privacy in Offline Reinforcement Learning |
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4 |
| Preserving Privacy in Large Language Models: A Survey on Current Threats and Solutions |
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0 |
| Preventing Conflicting Gradients in Neural Marked Temporal Point Processes |
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✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Prior Learning in Introspective VAEs |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Prior Specification for Exposure-based Bayesian Matrix Factorization |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| PrivShap: A Finer-granularity Network Linearization Method for Private Inference |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Privacy Awareness for Information-Sharing Assistants: A Case-study on Form-filling with Contextual Integrity |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
✅ |
2 |
| Privacy Risks and Preservation Methods in Explainable Artificial Intelligence: A Scoping Review |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Privacy-Aware Time Series Synthesis via Public Knowledge Distillation |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Private Fine-tuning of Large Language Models with Zeroth-order Optimization |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Private Regression via Data-Dependent Sufficient Statistic Perturbation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Private and Fair Machine Learning: Revisiting the Disparate Impact of Differentially Private SGD |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Probabilistic neural operators for functional uncertainty quantification |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Probabilities of Chat LLMs Are Miscalibrated but Still Predict Correctness on Multiple-Choice Q&A |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Producers Equilibria and Dynamics in Engagement-Driven Recommender Systems |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Prompt Engineering Techniques for Language Model Reasoning Lack Replicability |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Prompt Tuning Vision Language Models with Margin Regularizer for Few-Shot Learning under Distribution Shifts |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Provable Quantum Algorithm Advantage for Gaussian Process Quadrature |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Provable Robustness of (Graph) Neural Networks Against Data Poisoning and Backdoor Attacks |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
6 |
| Proximal Policy Distillation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Pruning Feature Extractor Stacking for Cross-domain Few-shot Learning |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Pseudo-Asynchronous Local SGD: Robust and Efficient Data-Parallel Training |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Pseudo-Physics-Informed Neural Operators: Enhancing Operator Learning from Limited Data |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Pushing the Limits of Sparsity: A Bag of Tricks for Extreme Pruning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| QPO: Query-dependent Prompt Optimization via Multi-Loop Offline Reinforcement Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Qualifying Knowledge and Knowledge Sharing in Multilingual Models |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Quantifying Context Bias in Domain Adaptation for Object Detection |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Quasipseudometric Value Functions with Dense Rewards |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| RANa: Retrieval-Augmented Navigation |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| RESTOR: Knowledge Recovery in Machine Unlearning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| REX: GPU-Accelerated Sim2Real Framework with Delay and Dynamics Estimation |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| RIZE: Adaptive Regularization for Imitation Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| RLeXplore: Accelerating Research in Intrinsically-Motivated Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| RNA-FrameFlow: Flow Matching for de novo 3D RNA Backbone Design |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| RS-Reg: Probabilistic and Robust Certified Regression through Randomized Smoothing |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Random Erasing vs. Model Inversion: A Promising Defense or a False Hope? |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Random Policy Enables In-Context Reinforcement Learning within Trust Horizons |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Random Walk Diffusion for Efficient Large-Scale Graph Generation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Rational Tuning of LLM Cascades via Probabilistic Modeling |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| ReDistill: Residual Encoded Distillation for Peak Memory Reduction of CNNs |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| ReFeR: Improving Evaluation and Reasoning through Hierarchy of Models |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| ReHub: Linear Complexity Graph Transformers with Adaptive Hub-Spoke Reassignment |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Real-Time Privacy Preservation for Robot Visual Perception |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Reasoning Under 1 Billion: Memory-Augmented Reinforcement Learning for Large Language Models |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Reassessing Fairness: A Reproducibility Study of NIFA’s Impact on GNN Models |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Rec-R1: Bridging Generative Large Language Models and User-Centric Recommendation Systems via Reinforcement Learning |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Recall and Refine: A Simple but Effective Source-free Open- set Domain Adaptation Framework |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquiry |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Rectified Robust Policy Optimization for Model-Uncertain Constrained Reinforcement Learning without Strong Duality |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
✅ |
4 |
| Recurrent Natural Policy Gradient for POMDPs |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Recursive SNE: Fast Prototype-Based t-SNE for Large-Scale and Online Data |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| RefDeblur: Blind Motion Deblurring with Self-Generated Reference Image |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Referential communication in heterogeneous communities of pre-trained visual deep networks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| RefinedFields: Radiance Fields Refinement for Planar Scene Representations |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Registers in Small Vision Transformers: A Reproducibility Study of Vision Transformers Need Registers |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Regret Analysis of Posterior Sampling-Based Expected Improvement for Bayesian Optimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Regularized Gradient Clipping Provably Trains Wide and Deep Neural Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Reheated Gradient-based Discrete Sampling for Combinatorial Optimization |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Reinforcement Learning for Causal Discovery without Acyclicity Constraints |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Reinforcement Learning from Bagged Reward |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Reinforcement Learning from Human Feedback with Active Queries |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Reinforcement learning with non-ergodic reward increments: robustness via ergodicity transformations |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Rel-HNN: Split Parallel Hypergraph Neural Network for Learning on Relational Databases |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Relationship between Batch Size and Number of Steps Needed for Nonconvex Optimization of Stochastic Gradient Descent using Armijo-Line-Search Learning Rate |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Relative Phase Equivariant Deep Neural Systems for Physical Layer Communications |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Relax and penalize: a new bilevel approach to mixed-binary hyperparameter optimization |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Reliable and Responsible Foundation Models |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Remembering to Be Fair Again: Reproducing Non-Markovian Fairness in Sequential Decision Making |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Removing Structured Noise using Diffusion Models |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Reproducibility Study of "Cooperation, Competition, and Maliciousness: LLM-Stakeholders Interactive Negotiation" |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Reproducibility Study of "Improving Interpretation Faithfulness For Vision Transformers" |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Reproducibility Study of ’SLICE: Stabilized LIME for Consistent Explanations for Image Classification’ |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Reproducibility study of "Competition of Mechanisms: Tracing How Language Models Handle Facts and Counterfactuals" |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Reset-free Reinforcement Learning with World Models |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| ResiDual Transformer Alignment with Spectral Decomposition |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Respecting the limit: Bayesian optimization with a bound on the optimal value |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Responsive Noise-Relaying Diffusion Policy: Responsive and Efficient Visuomotor Control |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Rethinking Knowledge Transfer in Learning Using Privileged Information |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Rethinking MUSHRA: Addressing Modern Challenges in Text-to-Speech Evaluation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Rethinking Memory in Continual Learning: Beyond a Monolithic Store of the Past |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Rethinking Patch Dependence for Masked Autoencoders |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Rethinking Robustness in Machine Learning: A Posterior Agreement Approach |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Rethinking Spectral Augmentation for Contrast-based Graph Self-Supervised Learning |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Rethinking the Value of Training-Free Structured Pruning of LLMs |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Retrieve, Merge, Predict: Augmenting Tables with Data Lakes |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
6 |
| Return-Aligned Decision Transformer |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Revisiting B2T: Discovering and Mitigating Visual Biases through Keyword Explanations |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Revisiting Contrastive Divergence for Density Estimation and Sample Generation |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Revisiting CroPA: A Reproducibility Study and Enhancements for Cross-Prompt Adversarial Transferability in Vision-Language Models |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Revisiting Data Augmentation for Ultrasound Images |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Revisiting Deep Hybrid Models for Out-of-Distribution Detection |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Revisiting Discover-then-Name Concept Bottleneck Models: A Reproducibility Study |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Revisiting XRec: How Collaborative Signals Influence LLM-Based Recommendation Explanations |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Reviving Life on the Edge: Joint Score-Based Graph Generation of Rich Edge Attributes |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Reward Distance Comparisons Under Transition Sparsity |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Reward-based Autonomous Online Learning Framework for Resilient Cooperative Target Monitoring using a Swarm of Robots |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Rewarding the Rare: Maverick-Aware Shapley Valuation in Federated Learning |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Reweighting Improves Conditional Risk Bounds |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Riemann-Lebesgue Forest for Regression |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Risk-controlling Prediction with Distributionally Robust Optimization |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Risk‑Seeking Reinforcement Learning via Multi‑Timescale EVaR Optimization |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| RoboRAN: A Unified Robotics Framework for Reinforcement Learning-Based Autonomous Navigation |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Robust High-Dimensional Mean Estimation With Low Data Size, an Empirical Study |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Robust Model Selection of Gaussian Graphical Models |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Robust Multimodal Learning via Cross-Modal Proxy Tokens |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Robust Offline Imitation Learning from Diverse Auxiliary Data |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Robust Preference Optimization through Reward Model Distillation |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Robust Reinforcement Learning in a Sample-Efficient Setting |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Robust Symbolic Regression for Dynamical System Identification |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Robust Weight Imprinting: Insights from Neural Collapse and Proxy-Based Aggregation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Robust and Efficient Fine-tuning of LLMs with Bayesian Reparameterization of Low-Rank Adaptation |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Robustness in Large Language Models: A Survey of Mitigation Strategies and Evaluation Metrics |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Rollout Total Correlation for Deep Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| RouteFinder: Towards Foundation Models for Vehicle Routing Problems |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| S-TLLR: STDP-inspired Temporal Local Learning Rule for Spiking Neural Networks |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| SAFE-NID: Self-Attention with Normalizing-Flow Encodings for Network Intrusion Detection |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| SAIF: Sparse Adversarial and Imperceptible Attack Framework |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| SCNode: Spatial and Contextual Coordinates for Graph Representation Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| SCas4D: Structural Cascaded Optimization for Boosting Persistent 4D Novel View Synthesis |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| SE3Set: Harnessing Equivariant Hypergraph Neural Networks for Molecular Representation Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| SEE-DPO: Self Entropy Enhanced Direct Preference Optimization |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| SELU: Self-Learning Embodied Multimodal Large Language Models in Unknown Environments |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| SETS: Leveraging Self-Verification and Self-Correction for Improved Test-Time Scaling |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| SIRE: SE(3) Intrinsic Rigidity Embeddings |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| SKADA-Bench: Benchmarking Unsupervised Domain Adaptation Methods with Realistic Validation On Diverse Modalities |
❌ |
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4 |
| SPFormer: Enhancing Vision Transformer with Superpixel Representation |
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3 |
| SPONGE: Competing Sparse Language Representations for Effective Knowledge Transfer |
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5 |
| SR-Reward: Taking The Path More Traveled |
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4 |
| STLDM: Spatio-Temporal Latent Diffusion Model for Precipitation Nowcasting |
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5 |
| SURE-VQA: Systematic Understanding of Robustness Evaluation in Medical VQA Tasks |
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4 |
| SaFARi: State-Space Models for Frame-Agnostic Representation |
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3 |
| Salsa Fresca: Angular Embeddings and Pre-Training for ML Attacks on Learning With Errors |
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3 |
| Sample, estimate, aggregate: A recipe for causal discovery foundation models |
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6 |
| Sample-efficient decoding of visual stimuli from fMRI through inter-individual functional alignment |
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4 |
| Say My Name: a Model's Bias Discovery Framework |
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4 |
| Scalable Generative Modeling of Weighted Graphs |
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6 |
| Scalable Multi-Output Gaussian Processes with Stochastic Variational Inference |
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5 |
| Scaling Channel-Adaptive Self-Supervised Learning |
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4 |
| Scaling Laws for Predicting Downstream Performance in LLMs |
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✅ |
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2 |
| Scaling Laws of Distributed Random Forests |
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7 |
| Scaling and Distilling Transformer Models for sEMG |
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6 |
| Schauder Bases for $C[0, 1]$ Using ReLU, Softplus and Two Sigmoidal Functions |
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0 |
| Score-Based Denoising Diffusion Models for Photon-Starved Image Restoration Problems |
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5 |
| Script: Graph-Structured and Query-Conditioned Semantic Token Pruning for Multimodal Large Language Models |
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4 |
| Seeing Beyond Labels: Source-Free Domain Adaptation via Hypothesis Consolidation of Prediction Rationale |
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6 |
| Segmenting Text and Learning Their Rewards for Improved RLHF in Language Model |
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4 |
| Selective Concept Bottleneck Models Without Predefined Concepts |
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5 |
| Selective Prediction via Training Dynamics |
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4 |
| Self-Exploring Language Models: Active Preference Elicitation for Online Alignment |
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5 |
| Self-Supervised Learning on Molecular Graphs: A Systematic Investigation of Masking Design |
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5 |
| SelfEval: Leveraging discriminative nature of generative models for evaluation |
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3 |
| Semantic Alignment for Prompt-Tuning in Vision Language Models |
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4 |
| Semantic Mapping in Indoor Embodied AI - A Survey on Advances, Challenges, and Future Directions |
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0 |
| Semantic-Syntactic Discrepancy in Images (SSDI): Learning Meaning and Order of Features from Natural Images |
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7 |
| Set-Based Training for Neural Network Verification |
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5 |
| Setting the Record Straight on Transformer Oversmoothing |
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3 |
| Shapley Values of Structured Additive Regression Models and Application to RKHS Weightings of Functions |
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6 |
| Shared Imagination: LLMs Hallucinate Alike |
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3 |
| Shared Stochastic Gaussian Process Latent Variable Models: A Multi-modal Generative model for Quasar spectra |
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6 |
| Shedding Light on Problems with Hyperbolic Graph Learning |
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3 |
| Show or Tell? Effectively prompting Vision-Language Models for semantic segmentation |
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5 |
| SimPLR: A Simple and Plain Transformer for Efficient Object Detection and Segmentation |
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4 |
| Simple Calibration via Geodesic Kernels |
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7 |
| Simple and Nearly-Optimal Sampling for Rank-1 Tensor Completion via Gauss-Jordan |
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1 |
| Simplifying Knowledge Transfer in Pretrained Models |
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6 |
| Simulation-based Bayesian Inference from Privacy Protected Data |
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6 |
| Single-pass Detection of Jailbreaking Input in Large Language Models |
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5 |
| Single-positive Multi-label Learning with Label Cardinality |
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5 |
| Slicing Unbalanced Optimal Transport |
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6 |
| Slicing the Gaussian Mixture Wasserstein Distance |
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5 |
| SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks |
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4 |
| Solution Augmentation for ARC Problems Using GFlowNet: A Probabilistic Exploration Approach |
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5 |
| Solving Inverse Problems using Diffusion with Iterative Colored Renoising |
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6 |
| Solving Multi-agent Path Finding as an LLM Benchmark: How, How Good and Why |
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3 |
| Solving Quadratic Programs via Deep Unrolled Douglas-Rachford Splitting |
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7 |
| Solving the Cold Start Problem on One's Own as an End User via Preference Transfer |
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4 |
| Sortability of Time Series Data |
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2 |
| SoundnessBench: A Soundness Benchmark for Neural Network Verifiers |
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5 |
| Spaced Scheduling for Large Language Model Training |
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7 |
| Sparse Decomposition of Graph Neural Networks |
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5 |
| Sparse Multiple Kernel Learning: Alternating Best Response and Semidefinite Relaxations |
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7 |
| Sparse Neural Architectures via Deterministic Ramanujan Graphs |
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3 |
| Sparse, Efficient and Explainable Data Attribution with DualXDA |
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5 |
| Sparse-Input Neural Network using Group Concave Regularization |
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4 |
| Sparse-to-Sparse Training of Diffusion Models |
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6 |
| SparseDiff: Sparse Discrete Diffusion for Scalable Graph Generation |
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6 |
| Sparser, Better, Faster, Stronger: Sparsity Detection for Efficient Automatic Differentiation |
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4 |
| Sparsified State-Space Models are Efficient Highway Networks |
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4 |
| Sparsity regularization via tree-structured environments for disentangled representations |
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5 |
| Sparsity-Driven Plasticity in Multi-Task Reinforcement Learning |
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3 |
| Spatio-temporal Partial Sensing Forecast of Long-term Traffic |
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5 |
| Spectral Clustering and Labeling for Crowdsourcing with Inherently Distinct Task Types |
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2 |
| Speech Synthesis By Unrolling Diffusion Process using Neural Network Layers |
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5 |
| SpidR: Learning Fast and Stable Linguistic Units for Spoken Language Models Without Supervision |
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6 |
| Spurious Privacy Leakage in Neural Networks |
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5 |
| Stability-Aware Training of Machine Learning Force Fields with Differentiable Boltzmann Estimators |
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4 |
| Stabilizing black-box model selection with the inflated argmax |
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4 |
| Stabilizing the Kumaraswamy Distribution |
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5 |
| Stacking Variational Bayesian Monte Carlo |
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5 |
| State Combinatorial Generalization In Decision Making With Conditional Diffusion Models |
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5 |
| State space models can express $n$-gram languages |
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2 |
| State-Constrained Offline Reinforcement Learning |
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6 |
| Statistical Error Bounds for GANs with Nonlinear Objective Functionals |
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0 |
| Statistical Guarantees for Approximate Stationary Points of Shallow Neural Networks |
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4 |
| Statistical Test for Saliency Maps of Graph Neural Networks via Selective Inference |
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5 |
| Step-Controlled DPO: Leveraging Stepwise Errors for Enhancing Mathematical Reasoning of Language Models |
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5 |
| Stochastic Block Model-Aware Topological Neural Networks for Graph Link Prediction |
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5 |
| Stochastic Primal-Dual Double Block-Coordinate for Two- way Partial AUC Maximization |
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4 |
| Stochastic Subspace Descent Accelerated via Bi-fidelity Line Search |
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4 |
| Stochastic Variance-Reduced Newton: Accelerating Finite-Sum Minimization with Large Batches |
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4 |
| Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models |
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1 |
| Streaming Heteroscedastic Probabilistic PCA with Missing Data |
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6 |
| Streamlining Language Models via Semantic Basis Analysis |
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5 |
| Structural Causal Circuits: Probabilistic Circuits Climbing All Rungs of Pearl's Ladder of Causation |
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4 |
| Studying Exploration in RL: An Optimal Transport Analysis of Occupancy Measure Trajectories |
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5 |
| Studying memorization of large language models using answers to Stack Overflow questions |
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3 |
| SuFP: Piecewise Bit Allocation Floating-Point for Robust Neural Network Quantization |
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3 |
| Successor Clusters: A Behavior Basis for Unsupervised Zero-Shot Reinforcement Learning |
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3 |
| Superposition as Lossy Compression — Measure with Sparse Autoencoders and Connect to Adversarial Vulnerability |
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3 |
| Survey of Video Diffusion Models: Foundations, Implementations, and Applications |
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4 |
| SynCode: LLM Generation with Grammar Augmentation |
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5 |
| Synchrony-Gated Plasticity with Dopamine Modulation for Spiking Neural Networks |
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7 |
| Synthesizing Minority Samples for Long-tailed Classification via Distribution Matching |
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6 |
| Synthesizing world models for bilevel planning |
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4 |
| Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models |
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5 |
| Synthetic Data is Sufficient for Zero-Shot Visual Generalization from Offline Data |
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1 |
| System-2 Mathematical Reasoning via Enriched Instruction Tuning |
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4 |
| System-Aware Neural ODE Processes for Few-Shot Bayesian Optimization |
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6 |
| T2L: Efficient Zero-Shot Action Recognition with Temporal Token Learning |
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5 |
| TACO Vision Models Can Be Efficiently Specialized via Few-Shot Task-Aware Compression |
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5 |
| TESGNN: Temporal Equivariant Scene Graph Neural Networks for Efficient and Robust Multi-View 3D Scene Understanding |
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5 |
| TFAR: A Training-Free Framework for Autonomous Reliable Reasoning in Visual Question Answering |
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6 |
| TP-Blend: Textual-Prompt Attention Pairing for Precise Object-Style Blending in Diffusion Models |
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4 |
| TRA: Better Length Generalisation with Threshold Relative Attention |
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6 |
| TRIDE: A Text-assisted Radar-Image weather-aware fusion network for Depth Estimation |
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5 |
| TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks |
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3 |
| TT-TFHE: a Torus Fully Homomorphic Encryption-Friendly Neural Network Architecture |
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❌ |
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5 |
| Table Foundation Models: on knowledge pre-training for tabular learning |
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5 |
| Tackling Feature and Sample Heterogeneity in Decentralized Multi-Task Learning: A Sheaf-Theoretic Approach |
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5 |
| Tackling the Abstraction and Reasoning Corpus with Vision Transformers: the Importance of 2D Representation, Positions, and Objects |
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5 |
| TapWeight: Reweighting Pretraining Objectives for Task-Adaptive Pretraining |
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6 |
| Targeted Unlearning Using Perturbed Sign Gradient Methods With Applications On Medical Images |
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5 |
| Task Arithmetic Through The Lens Of One-Shot Federated Learning |
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5 |
| Task Diversity Shortens the In-Context Learning Plateau |
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5 |
| Task-agnostic Prompt Compression with Context-aware Sentence Embedding and Reward-guided Task Descriptor |
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5 |
| Taxonomy, Opportunities, and Challenges of Representation Engineering for Large Language Models |
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❌ |
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1 |
| Teaching Diffusion Models to Ground Alpha Matte |
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5 |
| TempFlex: Advancing MLLMs with Temporal Perception and Natively Scalable Resolution Encoding |
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4 |
| Temporal Test-Time Adaptation with State-Space Models |
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5 |
| Temporal horizons in forecasting: a performance-learnability trade-off |
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❌ |
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5 |
| Test-Time Adaptation with Source Based Auxiliary Tasks |
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❌ |
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3 |
| Test-Time Fairness and Robustness in Large Language Models |
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4 |
| Test-time Contrastive Concepts for Open-world Semantic Segmentation with Vision-Language Models |
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✅ |
6 |
| Testing with Non-identically Distributed Samples |
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❌ |
❌ |
❌ |
❌ |
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❌ |
0 |
| Text to Stealthy Adversarial Face Masks |
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✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Text-to-Image Generation Via Energy-Based CLIP |
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❌ |
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5 |
| TextRegion: Text-Aligned Region Tokens from Frozen Image-Text Models |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| The 2023 Foundation Model Transparency Index |
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✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| The 2024 Foundation Model Transparency Index |
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✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| The AI Hippocampus: How Far are We From Human Memory? |
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✅ |
✅ |
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❌ |
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3 |
| The Accuracy Cost of Weakness: A Theoretical Analysis of Fixed-Segment Weak Labeling for Events in Time |
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✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| The BrowserGym Ecosystem for Web Agent Research |
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✅ |
✅ |
❌ |
✅ |
6 |
| The Choice of Normalization Influences Shrinkage in Regularized Regression |
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✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| The Curse of CoT: On the Limitations of Chain-of-Thought in In-Context Learning |
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✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| The Diffusion Process as a Correlation Machine: Linear Denoising Insights |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| The Elusive Pursuit of Reproducing PATE-GAN: Benchmarking, Auditing, Debugging |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| The Future of MLLM Prompting is Adaptive: A Comprehensive Experimental Evaluation of Prompt Engineering Methods for Robust Multimodal Performance |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
2 |
| The Geometry of Phase Transitions in Diffusion Models: Tubular Neighbourhoods and Singularities |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| The Initialization Determines Whether In-Context Learning Is Gradient Descent |
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❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| The Over-Certainty Phenomenon in Modern Test-Time Adaptation Algorithms |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| The Overcooked Generalisation Challenge: Evaluating Cooperation with Novel Partners in Unknown Environments Using Unsupervised Environment Design |
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✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| The Performance Of The Unadjusted Langevin Algorithm Without Smoothness Assumptions |
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❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| The RealHumanEval: Evaluating Large Language Models’ Abilities to Support Programmers |
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✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| The Sparse Matrix-Based Random Projection: A Study of Binary and Ternary Quantization |
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❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| The Time-Energy Model: Selective Time-Series Forecasting Using Energy-Based Models |
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✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| The inexact power augmented Lagrangian method for constrained nonconvex optimization |
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✅ |
✅ |
✅ |
❌ |
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6 |
| Theoretical Insights into Overparameterized Models in Multi-Task and Replay-Based Continual Learning |
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✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Theoretical Learning Performance of Graph Networks: the Impact of Jumping Connections and Layer-wise Sparsification |
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❌ |
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✅ |
❌ |
❌ |
✅ |
4 |
| Thera: Aliasing-Free Arbitrary-Scale Super-Resolution with Neural Heat Fields |
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❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| Thompson Sampling For Bandits With Cool-Down Periods |
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❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Thoughts and Lessons on Using Visual Foundation Models for Manipulation |
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❌ |
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❌ |
✅ |
4 |
| TicketLLM: Next-Generation Sparse and Low-bit Transformers with Supermask-based Method |
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✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Tighter sparse variational Gaussian processes |
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3 |
| Time Series Domain Adaptation via Channel-Selective Representation Alignment |
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✅ |
❌ |
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5 |
| Time-Uniform Confidence Spheres for Means of Random Vectors |
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✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| TimeAutoDiff: A Unified Framework for Generation, Imputation, Forecasting, and Time-Varying Metadata Conditioning of Heterogeneous Time Series Tabular Data |
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✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| To Be Greedy, or Not to Be – That Is the Question for Population Based Training Variants |
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✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Top-$k$ Feature Importance Ranking |
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✅ |
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❌ |
✅ |
6 |
| Toward Linearly Regularizing the Geometric Bottleneck of Linear Generalized Attention |
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❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Towards Better Understanding of In-Context Learning Ability from In-Context Uncertainty Quantification |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Towards Cross-Tokenizer Distillation: the Universal Logit Distillation Loss for LLMs |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Towards Efficient Contrastive PAC Learning |
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❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Towards Efficient Mixture of Experts: A Holistic Study of Compression Techniques |
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❌ |
❌ |
❌ |
✅ |
3 |
| Towards Efficient Training of Graph Neural Networks: A Multiscale Approach |
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✅ |
✅ |
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❌ |
❌ |
✅ |
5 |
| Towards Formalizing Spuriousness of Biased Datasets Using Partial Information Decomposition |
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✅ |
✅ |
❌ |
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6 |
| Towards Graph Foundation Models: A Study on the Generalization of Positional and Structural Encodings |
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❌ |
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❌ |
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✅ |
3 |
| Towards LifeSpan Cognitive Systems |
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❌ |
❌ |
❌ |
0 |
| Towards Measuring Predictability: To which extent data-driven approaches can extract deterministic relations from data exemplified with time series prediction and classification |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
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5 |
| Towards Robust Scale-Invariant Mutual Information Estimators |
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❌ |
❌ |
✅ |
3 |
| Towards Undistillable Models by Minimizing Conditional Mutual Information |
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✅ |
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✅ |
7 |
| Towards context and domain-aware algorithms for scene analysis |
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❌ |
❌ |
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5 |
| Towards identifiability of micro total effects in summary causal graphs with latent confounding: extension of the front-door criterion |
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❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Towards shutdownable agents via stochastic choice |
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❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Tracing Facts or just Copies? A critical investigation of the Competitions of Mechanisms in Large Language Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Tracking the Median of Gradients with a Stochastic Proximal Point Method |
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❌ |
✅ |
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❌ |
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3 |
| Tractable Representation Learning with Probabilistic Circuits |
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❌ |
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6 |
| Training Dynamics of Learning 3D-Rotational Equivariance |
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❌ |
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4 |
| Training Dynamics of the Cooldown Stage in Warmup-Stable-Decay Learning Rate Scheduler |
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❌ |
✅ |
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❌ |
❌ |
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3 |
| Transfer Learning in $\ell_1$ Regularized Regression: Hyperparameter Selection Strategy based on Sharp Asymptotic Analysis |
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❌ |
✅ |
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❌ |
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3 |
| Transferring Reasoning Capabilities between LLMs operating via Curriculum Learning Policy |
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✅ |
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5 |
| Transformers in Uniform TC$^0$ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Transformers trained on proteins can learn to attend to Euclidean distance |
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✅ |
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❌ |
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5 |
| Tree Search for Language Model Agents |
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✅ |
❌ |
❌ |
✅ |
5 |
| Tree Structure for the Categorical Wasserstein Weisfeiler-Lehman Graph Kernel |
✅ |
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✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Triple Preference Optimization: Achieving Better Alignment using a Single Step Optimization |
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❌ |
✅ |
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❌ |
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4 |
| Trustworthy and Responsible AI for Human-Centric Autonomous Decision-Making Systems |
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❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Two Is Better Than One: Aligned Representation Pairs for Anomaly Detection |
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❌ |
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❌ |
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4 |
| Two-Step Offline Preference-Based Reinforcement Learning on Explicitly Constrained Policies |
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❌ |
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❌ |
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4 |
| UMP-Net: Uncertainty-Aware Mixture of Prompts Network for Efficient Instruction Tuning |
✅ |
✅ |
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✅ |
✅ |
❌ |
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6 |
| UnSTAR: Unlearning with Self-Taught Anti-Sample Reasoning for LLMs |
✅ |
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✅ |
✅ |
✅ |
❌ |
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6 |
| Unbiased Loss Functions for Multilabel Classification with Missing Labels |
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✅ |
✅ |
✅ |
❌ |
❌ |
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4 |
| Uncertainty Quantification for Language Models: A Suite of Black-Box, White-Box, LLM Judge, and Ensemble Scorers |
❌ |
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✅ |
✅ |
✅ |
❌ |
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5 |
| Uncertainty Quantification in Retrieval Augmented Question Answering |
❌ |
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❌ |
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5 |
| Uncertainty Representations in State-Space Layers for Deep Reinforcement Learning under Partial Observability |
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❌ |
✅ |
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❌ |
❌ |
✅ |
3 |
| Uncertainty-Based Experience Replay for Task-Agnostic Continual Reinforcement Learning |
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❌ |
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4 |
| Uncertainty-aware Evaluation of Auxiliary Anomalies with the Expected Anomaly Posterior |
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❌ |
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4 |
| Uncertainty-aware Reward Design Process |
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6 |
| Uncovering Strong Lottery Tickets in Graph Transformers: A Path to Memory Efficient and Robust Graph Learning |
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❌ |
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❌ |
✅ |
3 |
| Understanding Class Bias Amplification in Graph Representation Learning |
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❌ |
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6 |
| Understanding Embedding Scaling in Collaborative Filtering |
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❌ |
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6 |
| Understanding Emergent In-Context Learning from a Kernel Regression Perspective |
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❌ |
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5 |
| Understanding Fine-tuning in Approximate Unlearning: A Theoretical Perspective |
❌ |
❌ |
✅ |
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❌ |
❌ |
✅ |
3 |
| Understanding In-Context Learning of Linear Models in Transformers Through an Adversarial Lens |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Understanding LLM Embeddings for Regression |
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✅ |
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❌ |
❌ |
✅ |
3 |
| Understanding Self-supervised Contrastive Learning through Supervised Objectives |
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✅ |
✅ |
❌ |
✅ |
5 |
| Understanding and Reducing the Class-Dependent Effects of Data Augmentation with A Two-Player Game Approach |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Understanding and Robustifying Sub-domain Alignment for Domain Adaptation |
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✅ |
❌ |
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❌ |
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5 |
| Understanding the learned look-ahead behavior of chess neural networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| UniTST: Effectively Modeling Inter-Series and Intra-Series Dependencies for Multivariate Time Series Forecasting |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| UniZero: Generalized and Efficient Planning with Scalable Latent World Models |
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✅ |
✅ |
❌ |
✅ |
❌ |
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5 |
| Unifi3D: A Study on 3D Representations for Generation and Reconstruction in a Common Framework |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Unified Preference Optimization: Language Model Alignment Beyond the Preference Frontier |
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❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Unified Risk Analysis for Weakly Supervised Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Unified Triplet-Level Hallucination Evaluation for Large Vision-Language Models |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Unified Wisdom: Harnessing Collaborative Learning to Improve Efficacy of Knowledge Distillation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Uniform Noise Distribution and Compact Clusters: Unveiling the Success of Self-Supervised Learning in Label Noise |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Unifying Generative and Dense Retrieval for Sequential Recommendation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Unifying Linear-Time Attention via Latent Probabilistic Modelling |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Unifying Self-Supervised Clustering and Energy-Based Models |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Universal Black-Box Targeted Reward Poisoning Attack Against Online Deep Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Universal Differential Equations for Stable Multi-Step Volatility Time Series Forecasting |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
4 |
| Universal Link Predictor By In-Context Learning on Graphs |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Universal and Efficient Detection of Adversarial Data through Nonuniform Impact on Network Layers |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Unlabelled Compressive Sensing under Sparse Permutation and Prior Information |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Unlearning Misalignment for Personalized LLM Adaptation via Instance-Response-Dependent Discrepancies |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Unlearning Personal Data from a Single Image |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Unleashing the Power of Data Tsunami: A Comprehensive Survey on Data Assessment and Selection for Instruction Tuning of Language Models |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Unlocking Visual Secrets: Inverting Features with Diffusion Priors for Image Reconstruction |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Unlocking the matrix form of the Quaternion Fourier Transform and Quaternion Convolution: Properties, connections, and application to Lipschitz constant bounding |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Unmasking Trees for Tabular Data |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Unreasonable effectiveness of LLM reasoning: a doubly cautionary tale of temporal question-answering |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Unsupervised Anomaly Detection through Mass Repulsing Optimal Transport |
✅ |
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✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Unsupervised Discovery of Object-Centric Neural Fields |
❌ |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
4 |
| Unsupervised Panoptic Interpretation of Latent Spaces in GANs Using Space-Filling Vector Quantization |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Unveiling Multiple Descents in Unsupervised Autoencoders |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Using Platt’s scaling for calibration after undersampling — limitations and how to address them |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
✅ |
3 |
| Using representation balancing to learn conditional-average dose responses from clustered data |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| VColRL: Learn to solve the Vertex Coloring Problem using Reinforcement Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| VLM’s Eye Examination: Instruct and Inspect Visual Competency of Vision Language Models |
❌ |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
3 |
| VSCoDe: Visual-Augmentation Selection for Contrastive Decoding |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Variance Dichotomy in Feature Spaces of Facial Recognition Systems is a Weak Defense against Simple Weight Manipulation Attacks |
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❌ |
✅ |
✅ |
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| Variance Reduced Smoothed Functional REINFORCE Policy Gradient Algorithms |
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| Variance Reduction of Stochastic Hypergradient Estimation by Mixed Fixed-Point Iteration |
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| Variation Matters: from Mitigating to Embracing Zero-Shot NAS Ranking Function Variation |
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| Variational Neural Stochastic Differential Equations with Change Points |
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| Variational Online Mirror Descent for Robust Learning in Schrödinger Bridge |
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| Variational Stochastic Gradient Descent for Deep Neural Networks |
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6 |
| Verbalized Machine Learning: Revisiting Machine Learning with Language Models |
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| ViTime: Foundation Model for Time Series Forecasting Powered by Vision Intelligence |
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| Video-Language Critic: Transferable Reward Functions for Language-Conditioned Robotics |
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5 |
| ViewFusion: Learning Composable Diffusion Models for Novel View Synthesis |
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| VirDA: Reusing Backbone for Unsupervised Domain Adaptation with Visual Reprogramming |
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5 |
| Vision-Language Models Provide Promptable Representations for Reinforcement Learning |
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6 |
| Visual Privacy Auditing with Diffusion Models |
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| Visual-Word Tokenizer: Beyond Fixed Sets of Tokens in Vision Transformers |
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5 |
| Visually Descriptive Language Model for Vector Graphics Reasoning |
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5 |
| Walking on the Fiber: A Simple Geometric Approximation for Bayesian Neural Networks |
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5 |
| Wasserstein Convergence of Score-based Generative Models under Semiconvexity and Discontinuous Gradients |
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| Wasserstein Coreset via Sinkhorn Loss |
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3 |
| Wasserstein Modality Alignment Makes Your Multimodal Transformer More Robust |
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4 |
| Weakly Supervised Object Segmentation by Background Conditional Divergence |
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6 |
| What Makes ImageNet Look Unlike LAION |
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4 |
| What Matters for Model Merging at Scale? |
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| What Should Embeddings Embed? Autoregressive Models Represent Latent Generating Distributions |
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5 |
| What Time Tells Us? An Explorative Study of Time Awareness Learned from Static Images |
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| What is the Relationship between Tensor Factorizations and Circuits (and How Can We Exploit it)? |
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6 |
| What’s Left After Distillation? How Knowledge Transfer Impacts Fairness and Bias |
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5 |
| When Are Bias-Free ReLU Networks Effectively Linear Networks? |
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| When Precision Meets Position: BFloat16 Breaks Down RoPE in Long-Context Training |
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| When SNN meets ANN: Error-Free ANN-to-SNN Conversion for Extreme Edge Efficiency |
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6 |
| When Should Reinforcement Learning Use Causal Reasoning? |
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| When resampling/reweighting improves feature learning in imbalanced classification? A toy-model study |
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| Where Do We Stand with Implicit Neural Representations? A Technical and Performance Survey |
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| Where are we with calibration under dataset shift in image classification? |
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| Where to Intervene: Action Selection in Deep Reinforcement Learning |
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| Which Backbone to Use: A Resource-efficient Domain Specific Comparison for Computer Vision |
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5 |
| Why Settle for Mid: A Probabilistic Viewpoint to Spatial Relationship Alignment in Text-to-image Models |
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4 |
| Why is constrained neural language generation particularly challenging? |
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| Wolf: Dense Video Captioning with a World Summarization Framework |
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5 |
| Wonderful Team: Zero-Shot Physical Task Planning with Visual LLMs |
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| YRC-Bench: A Benchmark for Learning to Coordinate with Experts |
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| YoooP: You Only Optimize One Prototype per Class for Non-Exemplar Incremental Learning |
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| Zero-1-to-G: Taming Pretrained 2D Diffusion Model for Direct 3D Generation |
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| Zero-shot CLIP Class Forgetting via Text-image Space Adaptation |
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| Zeroth-Order Adaptive Neuron Alignment Based Pruning without Re-Training |
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| Zoomer: Adaptive Image Focus Optimization for Black-box MLLM |
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| [RE] GNNBoundary: Finding Boundaries and Going Beyond Them |
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| [RE] GNNBoundary: Towards Explaining Graph Neural Networks through the Lens of Decision Boundaries |
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| [Re] Benchmarking LLM Capabilities in Negotiation through Scoreable Games |
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| [Re] Cooperate or Collapse: Emergence of Sustainable Cooperation in a Society of LLM Agents |
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| [Re] Improving Interpretation Faithfulness for Vision Transformers |
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| \copyright Plug-in Authorization for Human Copyright Protection in Text-to-Image Model |
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| crowd-hpo: Realistic Hyperparameter Optimization and Benchmarking for Learning from Crowds with Noisy Labels |
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5 |
| k-NN as a Simple and Effective Estimator of Transferability |
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| kNNSampler: Stochastic Imputations for Recovering Missing Value Distributions |
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5 |
| nnActive: A Framework for Evaluation of Active Learning in 3D Biomedical Segmentation |
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| νSAM: Memory-Efficient Sharpness-Aware Minimization via Nuclear Norm Constraints |
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| ∇QDARTS: Quantization as an Elastic Dimension to Differentiable NAS |
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5 |