| $\sigma$-PCA: a building block for neural learning of identifiable linear transformations |
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❌ |
✅ |
❌ |
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❌ |
✅ |
2 |
| 'Explaining RL Decisions with Trajectories’: A Reproducibility Study |
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❌ |
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5 |
| ***FastDoc***: Domain-Specific Fast Continual Pre-training Technique using Document-Level Metadata and Taxonomy |
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❌ |
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5 |
| 3D Molecular Generation via Virtual Dynamics |
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6 |
| A Bag of Tricks for Few-Shot Class-Incremental Learning |
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✅ |
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❌ |
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4 |
| A Distance-based Anomaly Detection Framework for Deep Reinforcement Learning |
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✅ |
4 |
| A Dual-Perspective Approach to Evaluating Feature Attribution Methods |
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❌ |
❌ |
✅ |
5 |
| A Fisher-Rao gradient flow for entropic mean-field min-max games |
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❌ |
❌ |
❌ |
0 |
| A Fully Decentralized Surrogate for Multi-Agent Policy Optimization |
✅ |
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✅ |
❌ |
✅ |
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✅ |
5 |
| A General-Purpose Multi-Modal OOD Detection Framework |
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✅ |
4 |
| A Globally Convergent Algorithm for Neural Network Parameter Optimization Based on Difference-of-Convex Functions |
✅ |
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✅ |
✅ |
❌ |
✅ |
6 |
| A Greedy Hierarchical Approach to Whole-Network Filter-Pruning in CNNs |
✅ |
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✅ |
❌ |
❌ |
✅ |
4 |
| A Joint Study of Phrase Grounding and Task Performance in Vision and Language Models |
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✅ |
❌ |
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5 |
| A Large-Scale 3D Face Mesh Video Dataset via Neural Re-parameterized Optimization |
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✅ |
3 |
| A Lennard-Jones Layer for Distribution Normalization |
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❌ |
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4 |
| A Multilinear Least-Squares Formulation for Sparse Tensor Canonical Correlation Analysis |
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✅ |
✅ |
7 |
| A Note on the Convergence of Denoising Diffusion Probabilistic Models |
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❌ |
✅ |
1 |
| A Practical Guide to Sample-based Statistical Distances for Evaluating Generative Models in Science |
❌ |
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❌ |
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✅ |
4 |
| A Probabilistic Model behind Self- Supervised Learning |
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❌ |
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6 |
| A Pseudo-Metric between Probability Distributions based on Depth-Trimmed Regions |
✅ |
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❌ |
✅ |
❌ |
✅ |
4 |
| A Review of the Applications of Deep Learning-Based Emergent Communication |
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❌ |
0 |
| A Self-Representation Learning Method for Unsupervised Feature Selection using Feature Space Basis |
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✅ |
❌ |
✅ |
✅ |
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6 |
| A Semi-Bayesian Nonparametric Estimator of the Maximum Mean Discrepancy Measure: Applications in Goodness-of-Fit Testing and Generative Adversarial Networks |
✅ |
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✅ |
✅ |
❌ |
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✅ |
5 |
| A Short Survey on Importance Weighting for Machine Learning |
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0 |
| A Simple Video Segmenter by Tracking Objects Along Axial Trajectories |
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❌ |
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5 |
| A Single Transformer for Scalable Vision-Language Modeling |
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❌ |
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6 |
| A Study of the Effects of Transfer Learning on Adversarial Robustness |
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4 |
| A Survey of Temporal Credit Assignment in Deep Reinforcement Learning |
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0 |
| A Survey on Compositional Learning of AI Models: Theoretical and Experimental Practices |
❌ |
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✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| A Survey on Data Selection for Language Models |
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✅ |
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1 |
| A Survey on Fairness Without Demographics |
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0 |
| A Survey on Graph Construction for Geometric Deep Learning in Medicine: Methods and Recommendations |
❌ |
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✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| A Survey on Large Language Models for Critical Societal Domains: Finance, Healthcare, and Law |
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❌ |
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0 |
| A Survey on Out-of-Distribution Detection in NLP |
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1 |
| A Survey on Transferability of Adversarial Examples Across Deep Neural Networks |
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1 |
| A Theoretical Framework for Zeroth-Order Budget Convex Optimization |
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2 |
| A Theoretical Study of The Effects of Adversarial Attacks on Sparse Regression |
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1 |
| A True-to-the-model Axiomatic Benchmark for Graph-based Explainers |
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3 |
| A Unified Hallucination Mitigation Framework for Large Vision-Language Models |
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6 |
| A Unified View of Differentially Private Deep Generative Modeling |
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0 |
| A Unified View on Solving Objective Mismatch in Model-Based Reinforcement Learning |
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1 |
| A VAE-based Framework for Learning Multi-Level Neural Granger-Causal Connectivity |
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5 |
| A density estimation perspective on learning from pairwise human preferences |
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3 |
| A general framework for formulating structured variable selection |
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0 |
| A note on regularised NTK dynamics with an application to PAC-Bayesian training |
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0 |
| A persistent homology-based algorithm for unsupervised anomaly detection in time series |
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4 |
| A replica analysis of under-bagging |
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3 |
| AGALE: A Graph-Aware Continual Learning Evaluation Framework |
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4 |
| AGG: Amortized Generative 3D Gaussians for Single Image to 3D |
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4 |
| AGaLiTe: Approximate Gated Linear Transformers for Online Reinforcement Learning |
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5 |
| APBench: A Unified Availability Poisoning Attack and Defenses Benchmark |
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4 |
| ASPEST: Bridging the Gap Between Active Learning and Selective Prediction |
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7 |
| Accelerated Deep Active Learning with Graph-based Sub- Sampling |
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3 |
| Accountable Textual-Visual Chat Learns to Reject Human Instructions in Image Re-creation |
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5 |
| Accurate Neural Network Pruning Requires Rethinking Sparse Optimization |
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❌ |
✅ |
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4 |
| Achieving the Asymptotically Minimax Optimal Sample Complexity of Offline Reinforcement Learning: A DRO-Based Approach |
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2 |
| Active Learning for Level Set Estimation Using Randomized Straddle Algorithms |
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3 |
| Active Sequential Two-Sample Testing |
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3 |
| AdaFed: Fair Federated Learning via Adaptive Common Descent Direction |
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4 |
| AdaFlood: Adaptive Flood Regularization |
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4 |
| AdaStop: adaptive statistical testing for sound comparisons of Deep RL agents |
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4 |
| AdaWaveNet: Adaptive Wavelet Network for Time Series Analysis |
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6 |
| Adapting Contrastive Language-Image Pretrained (CLIP) Models for Out-of-Distribution Detection |
❌ |
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✅ |
✅ |
❌ |
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5 |
| Adaptive Conformal Regression with Split-Jackknife+ Scores |
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6 |
| Adaptive Self-Distillation for Minimizing Client Drift in Heterogeneous Federated Learning |
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✅ |
✅ |
❌ |
❌ |
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4 |
| Adaptive Training Distributions with Scalable Online Bilevel Optimization |
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✅ |
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❌ |
❌ |
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5 |
| Adaptively Robust and Sparse $K$-means Clustering |
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✅ |
❌ |
❌ |
❌ |
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4 |
| Addressing Attribute Bias with Adversarial Support-Matching |
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✅ |
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❌ |
❌ |
✅ |
5 |
| Adversarial Attacks on Online Learning to Rank with Stochastic Click Models |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
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4 |
| Adversarial Imitation Learning from Visual Observations using Latent Information |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
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4 |
| Adversarially Robust Spiking Neural Networks Through Conversion |
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7 |
| Affordable Generative Agents |
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❌ |
❌ |
❌ |
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3 |
| AmbientFlow: Invertible generative models from incomplete, noisy measurements |
❌ |
✅ |
✅ |
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❌ |
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5 |
| Amortized Bayesian Decision Making for simulation-based models |
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❌ |
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6 |
| An Attentive Approach for Building Partial Reasoning Agents from Pixels |
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❌ |
❌ |
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5 |
| An Improved Federated Clustering Algorithm with Model-based Clustering |
✅ |
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✅ |
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❌ |
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5 |
| An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces |
✅ |
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✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| An optimal control perspective on diffusion-based generative modeling |
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❌ |
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4 |
| Analyzing Deep Transformer Models for Time Series Forecasting via Manifold Learning |
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✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Analyzing the Impact of Learnable Softmax Temperature in Contrastive Visual-Textual Alignment Systems: Benefits, Drawbacks, and Alternative Approaches |
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✅ |
✅ |
✅ |
✅ |
❌ |
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6 |
| Anomaly detection with semi-supervised classification based on risk estimators |
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❌ |
✅ |
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3 |
| Anticipatory Music Transformer |
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❌ |
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6 |
| AnyV2V: A Tuning-Free Framework For Any Video-to-Video Editing Tasks |
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✅ |
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❌ |
✅ |
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4 |
| Application of Bagged Copula-GP: Confirming Neural Dependency on Pupil Dilation |
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7 |
| Appropriate Balance of Diversification and Intensification Improves Performance and Efficiency of Adversarial Attacks |
✅ |
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✅ |
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❌ |
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5 |
| Approximations to the Fisher Information Metric of Deep Generative Models for Out-Of-Distribution Detection |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Archetypal Analysis++: Rethinking the Initialization Strategy |
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5 |
| Are Population Graphs Really as Powerful as Believed? |
❌ |
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5 |
| Are you using test log-likelihood correctly? |
❌ |
❌ |
❌ |
✅ |
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❌ |
✅ |
2 |
| As large as it gets – Studying Infinitely Large Convolutions via Neural Implicit Frequency Filters |
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✅ |
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5 |
| Assessing Robustness via Score-Based Adversarial Image Generation |
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5 |
| Assessing biomedical knowledge robustness in large language models by query-efficient sampling attacks |
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4 |
| Asynchronous Training Schemes in Distributed Learning with Time Delay |
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4 |
| Attacking Bayes: On the Adversarial Robustness of Bayesian Neural Networks |
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4 |
| Attending to Graph Transformers |
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5 |
| Attention Normalization Impacts Cardinality Generalization in Slot Attention |
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5 |
| Attribute Graphs Underlying Molecular Generative Models: Path to Learning with Limited Data |
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5 |
| Audio-Visual Dataset Distillation |
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6 |
| Augment then Smooth: Reconciling Differential Privacy with Certified Robustness |
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7 |
| Augmenting Ad-Hoc IR Dataset for Interactive Conversational Search |
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5 |
| AutoCLIP: Auto-tuning Zero-Shot Classifiers for Vision-Language Models |
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7 |
| AutoDocSegmenter: A Geometric Approach towards Self-Supervised Document Segmentation |
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❌ |
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5 |
| AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks |
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0 |
| Autoencoding Hyperbolic Representation for Adversarial Generation |
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3 |
| Automated Design of Metaheuristic Algorithms: A Survey |
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0 |
| Automatic Data Curation for Self-Supervised Learning: A Clustering-Based Approach |
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❌ |
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5 |
| BBCaL: Black-box Backdoor Detection under the Causality Lens |
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5 |
| BP($\mathbf{\lambda}$): Online Learning via Synthetic Gradients |
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✅ |
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5 |
| BaSIS-Net: From Point Estimate to Predictive Distribution in Neural Networks - A Bayesian Sequential Importance Sampling Framework |
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4 |
| Bandits Corrupted by Nature: Lower Bounds on Regret and Robust Optimistic Algorithms |
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4 |
| Bandits with Mean Bounds |
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3 |
| Bayesian Computation Meets Topology |
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3 |
| Bayesian Quantification with Black-Box Estimators |
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6 |
| Bayesian optimization with derivatives acceleration |
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2 |
| Best-of-Both-Worlds Linear Contextual Bandits |
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1 |
| Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models |
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5 |
| Beyond Labeling Oracles - What does it mean to steal ML models? |
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3 |
| Beyond Loss Functions: Exploring Data-Centric Approaches with Diffusion Model for Domain Generalization |
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4 |
| Beyond Text: Utilizing Vocal Cues to Improve Decision Making in LLMs for Robot Navigation Tasks |
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4 |
| Bias Amplification Enhances Minority Group Performance |
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5 |
| Bias/Variance is not the same as Approximation/Estimation |
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2 |
| Biased Dueling Bandits with Stochastic Delayed Feedback |
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3 |
| Bit-by-Bit: Investigating the Vulnerabilities of Binary Neural Networks to Adversarial Bit Flipping |
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4 |
| Blending Two Styles: Generating Inter-domain Images with MiddleGAN |
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4 |
| Blind Biological Sequence Denoising with Self-Supervised Set Learning |
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✅ |
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2 |
| Blockwise Self-Supervised Learning at Scale |
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✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Boomerang: Local sampling on image manifolds using diffusion models |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
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5 |
| Boosting Data-Driven Mirror Descent with Randomization, Equivariance, and Acceleration |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Boosting Unsupervised Semantic Segmentation with Principal Mask Proposals |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Break it, Imitate it, Fix it: Robustness by Generating Human-Like Attacks |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Budget-Aware Sequential Brick Assembly with Efficient Constraint Satisfaction |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Budgeted Online Model Selection and Fine-Tuning via Federated Learning |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Bytes Are All You Need: Transformers Operating Directly On File Bytes |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Byzantine-Resilient Decentralized Multi-Armed Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| C3DM: Constrained-Context Conditional Diffusion Models for Imitation Learning |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| CAREER: A Foundation Model for Labor Sequence Data |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| CFASL: Composite Factor-Aligned Symmetry Learning for Disentanglement in Variational AutoEncoder |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| CLIP meets Model Zoo Experts: Pseudo-Supervision for Visual Enhancement |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| CLIP-QDA: An Explainable Concept Bottleneck Model |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| CORE-Bench: Fostering the Credibility of Published Research Through a Computational Reproducibility Agent Benchmark |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| CR-MoE: Consistent Routed Mixture-of-Experts for Scaling Contrastive Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| CREW: Facilitating Human-AI Teaming Research |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
5 |
| Calibrated Uncertainty Quantification for Operator Learning via Conformal Prediction |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Calibrating Deep Ensemble through Functional Variational Inference |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Calibration Attacks: A Comprehensive Study of Adversarial Attacks on Model Confidence |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Can LLMs Effectively Leverage Graph Structural Information through Prompts, and Why? |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Can We Count on LLMs? The Fixed-Effect Fallacy and Claims of GPT-4 Capabilities |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Candidate Set Re-ranking for Composed Image Retrieval with Dual Multi-modal Encoder |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| CascadedGaze: Efficiency in Global Context Extraction for Image Restoration |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Causal Discovery from Time Series with Hybrids of Constraint-Based and Noise-Based Algorithms |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Causal Reasoning and Large Language Models: Opening a New Frontier for Causality |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Certified Deductive Reasoning with Language Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Certified Robustness against Sparse Adversarial Perturbations via Data Localization |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Chain-of-Thought Unfaithfulness as Disguised Accuracy |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| ChatGPT Asks, BLIP-2 Answers: Automatic Questioning Towards Enriched Visual Descriptions |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Choosing Wisely and Learning Deeply: Selective Cross-Modality Distillation via CLIP for Domain Generalization |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Choosing the parameter of the Fermat distance: navigating geometry and noise |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Chronos: Learning the Language of Time Series |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| CiPR: An Efficient Framework with Cross-instance Positive Relations for Generalized Category Discovery |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Class-Discriminative Attention Maps for Vision Transformers |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Closing the gap between SVRG and TD-SVRG with Gradient Splitting |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| CoDeC: Communication-Efficient Decentralized Continual Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| CoMIX: A Multi-agent Reinforcement Learning Training Architecture for Efficient Decentralized Coordination and Independent Decision-Making |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Cognitive Architectures for Language Agents |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Combine and Conquer: A Meta-Analysis on Data Shift and Out-of-Distribution Detection |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Comparing Deterministic and Soft Policy Gradients for Optimizing Gaussian Mixture Actors |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| CompoDiff: Versatile Composed Image Retrieval With Latent Diffusion |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Compositional Instruction Following with Language Models and Reinforcement Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Compressing the Activation Maps in Deep Convolutional Neural Networks and Its Regularizing Effect |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Concept-Driven Continual Learning |
✅ |
✅ |
✅ |
✅ |
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❌ |
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6 |
| Conciliator steering: Imposing user preference in multi-objective reinforcement learning |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Confidence Intervals and Simultaneous Confidence Bands Based on Deep Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Confidence-aware Denoised Fine-tuning of Off-the-shelf Models for Certified Robustness |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Conservative Evaluation of Offline Policy Learning |
✅ |
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✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Conservative Prediction via Data-Driven Confidence Minimization |
✅ |
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✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| ConsistI2V: Enhancing Visual Consistency for Image-to-Video Generation |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Constraining Generative Models for Engineering Design with Negative Data |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Contaminated Online Convex Optimization |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
✅ |
4 |
| Contextual Policies Enable Efficient and Interpretable Inverse Reinforcement Learning for Populations |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Continual Adaptation of Vision Transformers for Federated Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Continual Diffusion: Continual Customization of Text-to-Image Diffusion with C-LoRA |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Continual HyperTransformer: A Meta-Learner for Continual Few-Shot Learning |
✅ |
❌ |
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❌ |
❌ |
❌ |
✅ |
3 |
| Continual Learning in Open-vocabulary Classification with Complementary Memory Systems |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Continual Learning: Applications and the Road Forward |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Continuous U-Net: Faster, Greater and Noiseless |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Contrastive Class Anchor Learning for Open Set Object Recognition in Driving Scenes |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Contrastive Graph Autoencoder for Shape-based Polygon Retrieval from Large Geometry Datasets |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Contrastive Learning with Adaptive Neighborhoods for Brain Age Prediction on 3D Stiffness Maps |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Contrastive Learning with Consistent Representations |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Controlling Federated Learning for Covertness |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Controlling the Fidelity and Diversity of Deep Generative Models via Pseudo Density |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Controlling the Inductive Bias of Wide Neural Networks by Modifying the Kernel’s Spectrum |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Convergence Analysis and Trajectory Comparison of Gradient Descent for Overparameterized Deep Linear Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Convergence Analysis of Fractional Gradient Descent |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Convergences for Minimax Optimization Problems over Infinite-Dimensional Spaces Towards Stability in Adversarial Training |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Cooperative Online Learning with Feedback Graphs |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Coordinate Transform Fourier Neural Operators for Symmetries in Physical Modelings |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Correcting Flaws in Common Disentanglement Metrics |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Corrective Machine Unlearning |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| Correlation Clustering with Active Learning of Pairwise Similarities |
✅ |
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❌ |
❌ |
❌ |
✅ |
3 |
| Cost-Sensitive Learning to Defer to Multiple Experts with Workload Constraints |
✅ |
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✅ |
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✅ |
❌ |
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6 |
| Credal Bayesian Deep Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| DDLP: Unsupervised Object-centric Video Prediction with Deep Dynamic Latent Particles |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| DFML: Decentralized Federated Mutual Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| DIG In: Evaluating Disparities in Image Generations with Indicators for Geographic Diversity |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| DIG-MILP: a Deep Instance Generator for Mixed-Integer Linear Programming with Feasibility Guarantee |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
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6 |
| DIGNet: Learning Decomposed Patterns in Representation Balancing for Treatment Effect Estimation |
❌ |
❌ |
✅ |
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✅ |
❌ |
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4 |
| DINOv2: Learning Robust Visual Features without Supervision |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
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6 |
| DP-ImgSyn: Dataset Alignment for Obfuscated, Differentially Private Image Synthesis |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| DSI2I: Dense Style for Unpaired Exemplar-based Image-to- Image Translation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| DTRNet: Precisely Correcting Selection Bias in Individual-Level Continuous Treatment Effect Estimation by Reweighted Disentangled Representation |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Data Attribution for Diffusion Models: Timestep-induced Bias in Influence Estimation |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
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4 |
| Data Pruning Can Do More: A Comprehensive Data Pruning Approach for Object Re-identification |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Data Valuation in the Absence of a Reliable Validation Set |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Data-Centric Defense: Shaping Loss Landscape with Augmentations to Counter Model Inversion |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Data-Dependent Generalization Bounds for Neural Networks with ReLU |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Dataset Distillation via Curriculum Data Synthesis in Large Data Era |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Decentralized Decoupled Training for Federated Long-Tailed Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Decomposition of Equivariant Maps via Invariant Maps: Application to Universal Approximation under Symmetry. |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Deconfounding Imitation Learning with Variational Inference |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Decoupling Pixel Flipping and Occlusion Strategy for Consistent XAI Benchmarks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Deep Backtracking Counterfactuals for Causally Compliant Explanations |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Deep End-to-end Causal Inference |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Deep Generalized Prediction Set Classifier and Its Theoretical Guarantees |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Deep Generative Models for Offline Policy Learning: Tutorial, Survey, and Perspectives on Future Directions |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Deep Generative Models through the Lens of the Manifold Hypothesis: A Survey and New Connections |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Deep Kernel Learning of Nonlinear Latent Force Models |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Deep Tabular Learning via Distillation and Language Guidance |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
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5 |
| Deep Unlearning: Fast and Efficient Gradient-free Class Forgetting |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Deep-Graph-Sprints: Accelerated Representation Learning in Continuous-Time Dynamic Graphs |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| DeepReShape: Redesigning Neural Networks for Efficient Private Inference |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
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6 |
| Defending Against Unknown Corrupted Agents: Reinforcement Learning of Adversarially Robust Nash Equilibria |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Demographically-Informed Prediction Discrepancy Index: Early Warnings of Demographic Biases for Unlabeled Populations |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Demonstrating and Reducing Shortcuts in Vision-Language Representation Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Demonstration-Guided Multi-Objective Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Dependency Structure Search Bayesian Optimization for Decision Making Models |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
5 |
| Depth Scaling in Graph Neural Networks: Understanding the Flat Curve Behavior |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Differential Equation Scaling Limits of Shaped and Unshaped Neural Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Differentially Private Kernel Inducing Points using features from ScatterNets (DP-KIP-ScatterNet) for Privacy Preserving Data Distillation |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Differentially Private Latent Diffusion Models |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Differentiating Through Integer Linear Programs with Quadratic Regularization and Davis-Yin Splitting |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Diffusion Models with Deterministic Normalizing Flow Priors |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Directed Graph Transformers |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Directional Convergence Near Small Initializations and Saddles in Two-Homogeneous Neural Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Discffusion: Discriminative Diffusion Models as Few-shot Vision and Language Learners |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Disciplined Saddle Programming |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Discovering Model Structure of Dynamical Systems with Combinatorial Bayesian Optimization |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Discrete Graph Auto-Encoder |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Discriminative reconstruction via simultaneous dense and sparse coding |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Distributional GFlowNets with Quantile Flows |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Distributionally Robust Policy Evaluation under General Covariate Shift in Contextual Bandits |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Diversity-Preserving $K$--Armed Bandits, Revisited |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Do Parameters Reveal More than Loss for Membership Inference? |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Do not trust what you trust: Miscalibration in Semisupervised Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Does Representation Similarity Capture Function Similarity? |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Domain-Generalizable Multiple-Domain Clustering |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Double Descent and Overfitting under Noisy Inputs and Distribution Shift for Linear Denoisers |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Doubly Robust Kernel Statistics for Testing Distributional Treatment Effects |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| DrGNN: Deep Residual Graph Neural Network with Contrastive Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Dual-windowed Vision Transformer with Angular Self- Attention |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| DyG2Vec: Efficient Representation Learning for Dynamic Graphs |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| DynaConF: Dynamic Forecasting of Non-Stationary Time Series |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Dynamic Online Ensembles of Basis Expansions |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Dynamic Structure Estimation from Bandit Feedback using Nonvanishing Exponential Sums |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| E(n)-equivariant Graph Neural Cellular Automata |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| E-Valuating Classifier Two-Sample Tests |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| ECG Semantic Integrator (ESI): A Foundation ECG Model Pretrained with LLM-Enhanced Cardiological Text |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| EHI: End-to-end Learning of Hierarchical Index for Efficient Dense Retrieval |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| EHRDiff : Exploring Realistic EHR Synthesis with Diffusion Models |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Effective Latent Differential Equation Models via Attention and Multiple Shooting |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
4 |
| Efficient Action Robust Reinforcement Learning with Probabilistic Policy Execution Uncertainty |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Efficient Identification of Direct Causal Parents via Invariance and Minimum Error Testing |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Efficient Large Language Models: A Survey |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Efficient Model-Agnostic Multi-Group Equivariant Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Efficient Parallelized Simulation of Cyber-Physical Systems |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Embracing Unknown Step by Step: Towards Reliable Sparse Training in Real World |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Empowering GNNs via Edge-Aware Weisfeiler-Leman Algorithm |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| End-to-End Training Induces Information Bottleneck through Layer-Role Differentiation: A Comparative Analysis with Layer-wise Training |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Enhancing Compositional Generalization via Compositional Feature Alignment |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Enhancing Contrastive Clustering with Negative Pair-guided Regularization |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Enhancing Low-Precision Sampling via Stochastic Gradient Hamiltonian Monte Carlo |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Enhancing Robustness to Class-Conditional Distribution Shift in Long-Tailed Recognition |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Enhancing Vision-Language Model with Unmasked Token Alignment |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Equivariant Graph Learning for High-density Crowd Trajectories Modeling |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Equivariant Graph Network Approximations of High-Degree Polynomials for Force Field Prediction |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Equivariant Symmetry Breaking Sets |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
4 |
| Error Bounds for Flow Matching Methods |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Estimating Optimal Policy Value in Linear Contextual Bandits Beyond Gaussianity |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Estimating class separability of text embeddings with persistent homology. |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
4 |
| Evaluating Graph Generative Models with Graph Kernels: What Structural Characteristics Are Captured? |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Evaluating Spatial Understanding of Large Language Models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Evaluating the Evaluators: Are Validation Methods for Few-Shot Learning Fit for Purpose? |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Exact Fractional Inference via Re-Parametrization \& Interpolation between Tree-Re-Weighted- and Belief Propagation- Algorithms |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Expected Pinball Loss For Quantile Regression And Inverse CDF Estimation |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Exploit CAM by itself: Complementary Learning System for Weakly Supervised Semantic Segmentation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Exploiting Edge Features in Graph-based Learning with Fused Network Gromov-Wasserstein Distance |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Exploiting Hankel-Toeplitz Structures for Fast Computation of Kernel Precision Matrices |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Exploring Format Consistency for Instruction Tuning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Exploring Human-in-the-Loop Test-Time Adaptation by Synergizing Active Learning and Model Selection |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Exploring Simple, High Quality Out-of-Distribution Detection with L2 Normalization |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Exploring validation metrics for offline model-based optimisation with diffusion models |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Exponential Moving Average of Weights in Deep Learning: Dynamics and Benefits |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Exposing Limitations of Language Model Agents in Sequential-Task Compositions on the Web |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Exposing and Addressing Cross-Task Inconsistency in Unified Vision-Language Models |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Expressive Higher-Order Link Prediction through Hypergraph Symmetry Breaking |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Extended Deep Submodular Functions |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
4 |
| Extending Path-Dependent NJ-ODEs to Noisy Observations and a Dependent Observation Framework |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Extreme Risk Mitigation in Reinforcement Learning using Extreme Value Theory |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| FLR: Label-Mixture Regularization for Federated Learning with Noisy Labels |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Fair Feature Importance Scores for Interpreting Decision Trees |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Fair Representation in Submodular Subset Selection: A Pareto Optimization Approach |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Fairness Under Demographic Scarce Regime |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Fast Computation of Leave-One-Out Cross-Validation for $k$-NN Regression |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Fast Training of Diffusion Models with Masked Transformers |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Fast and Effective Weight Update for Pruned Large Language Models |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Fast and Expressive Gesture Recognition using a Combination-Homomorphic Electromyogram Encoder |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Fast, accurate and lightweight sequential simulation-based inference using Gaussian locally linear mappings |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Faster Convergence of Local SGD for Over-Parameterized Models |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Faster optimal univariate microaggregation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Feature Alignment: Rethinking Efficient Active Learning via Proxy in the Context of Pre-trained Models |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Feature Distillation Improves Zero-Shot Transfer from Synthetic Images |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Feature learning as alignment: a structural property of gradient descent in non-linear neural networks |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| FedConv: Enhancing Convolutional Neural Networks for Handling Data Heterogeneity in Federated Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Federated $\mathcal{X}$-armed Bandit with Flexible Personalisation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Federated Classification in Hyperbolic Spaces via Secure Aggregation of Convex Hulls |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Federated Graph Learning with Graphless Clients |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Federated Learning with Convex Global and Local Constraints |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Federated Learning with Reduced Information Leakage and Computation |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Federated Sampling with Langevin Algorithm under Isoperimetry |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Federated TD Learning with Linear Function Approximation under Environmental Heterogeneity |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Federated Variational Inference: Towards Improved Personalization and Generalization |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Feedback-guided Data Synthesis for Imbalanced Classification |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Feudal Graph Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Fine-tuning can cripple your foundation model; preserving features may be the solution |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Finite-Time Analysis of Entropy-Regularized Neural Natural Actor-Critic Algorithm |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Finite-Time Analysis of Temporal Difference Learning with Experience Replay |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Fixed Budget Best Arm Identification in Unimodal Bandits |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Fixed-Budget Best-Arm Identification in Sparse Linear Bandits |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| FlexEControl: Flexible and Efficient Multimodal Control for Text-to-Image Generation |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Fooling Contrastive Language-Image Pre-Trained Models with CLIPMasterPrints |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| For Robust Worst-Group Accuracy, Ignore Group Annotations |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Foundational Challenges in Assuring Alignment and Safety of Large Language Models |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| From Complexity to Clarity: Analytical Expressions of Deep Neural Network Weights via Clifford Algebra and Convexity |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| From Continuous Dynamics to Graph Neural Networks: Neural Diffusion and Beyond |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Models |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| From Differential Privacy to Bounds on Membership Inference: Less can be More |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| From Identifiable Causal Representations to Controllable Counterfactual Generation: A Survey on Causal Generative Modeling |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| From Persona to Personalization: A Survey on Role-Playing Language Agents |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| From Stability to Chaos: Analyzing Gradient Descent Dynamics in Quadratic Regression |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Function Basis Encoding of Numerical Features in Factorization Machines |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Functional Linear Regression of Cumulative Distribution Functions |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Fundamental Problems With Model Editing: How Should Rational Belief Revision Work in LLMs? |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| G4SATBench: Benchmarking and Advancing SAT Solving with Graph Neural Networks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| GCondNet: A Novel Method for Improving Neural Networks on Small High-Dimensional Tabular Data |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| GLASU: A Communication-Efficient Algorithm for Federated Learning with Vertically Distributed Graph Data |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| GOPlan: Goal-conditioned Offline Reinforcement Learning by Planning with Learned Models |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| GSURE-Based Diffusion Model Training with Corrupted Data |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| GUARD: A Safe Reinforcement Learning Benchmark |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Gaussian-Smoothed Sliced Probability Divergences |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Generalization Bounds with Logarithmic Negative-Sample Dependence for Adversarial Contrastive Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Generalized Oversampling for Learning from Imbalanced datasets and Associated Theory: Application in Regression |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Generalizing Denoising to Non-Equilibrium Structures Improves Equivariant Force Fields |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Generalizing Neural Additive Models via Statistical Multimodal Analysis |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Generating Less Certain Adversarial Examples Improves Robust Generalization |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Generating with Confidence: Uncertainty Quantification for Black-box Large Language Models |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Generative Models are Self-Watermarked: Declaring Model Authentication through Re-Generation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Genetic InfoMax: Exploring Mutual Information Maximization in High-Dimensional Imaging Genetics Studies |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
5 |
| Geometrical aspects of lattice gauge equivariant convolutional neural networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Global Convergence Guarantees for Federated Policy Gradient Methods with Adversaries |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Global Convergence of SGD For Logistic Loss on Two Layer Neural Nets |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Gradient Scarcity in Graph Learning with Bilevel Optimization |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Gradient-guided discrete walk-jump sampling for biological sequence generation |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Granger Causal Interaction Skill Chains |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Graph Cuts with Arbitrary Size Constraints Through Optimal Transport |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Graph Harmony: Denoising and Nuclear-Norm Wasserstein Adaptation for Enhanced Domain Transfer in Graph-Structured Data |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Graph Knowledge Distillation to Mixture of Experts |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Graph Neural Networks Formed via Layer-wise Ensembles of Heterogeneous Base Models |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Graph Pooling via Ricci Flow |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Graph Reinforcement Learning for Combinatorial Optimization: A Survey and Unifying Perspective |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| GraphMaker: Can Diffusion Models Generate Large Attributed Graphs? |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| GraphPrivatizer: Improved Structural Differential Privacy for Graph Neural Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Graphon-Explainer: Generating Model-Level Explanations for Graph Neural Networks using Graphons |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Greedy Growing Enables High-Resolution Pixel-Based Diffusion Models |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Grid Cell-Inspired Fragmentation and Recall for Efficient Map Building |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Grokking Beyond Neural Networks: An Empirical Exploration with Model Complexity |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Gromov-Wasserstein-like Distances in the Gaussian Mixture Models Space |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Group Fairness in Reinforcement Learning via Multi-Objective Rewards |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Growing Tiny Networks: Spotting Expressivity Bottlenecks and Fixing Them Optimally |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
5 |
| Guarantees of confidentiality via Hammersley-Chapman-Robbins bounds |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| HQ-VAE: Hierarchical Discrete Representation Learning with Variational Bayes |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Harnessing the Power of Federated Learning in Federated Contextual Bandits |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Hashing with Uncertainty Quantification via Sampling-based Hypothesis Testing |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Hessian Free Efficient Single Loop Iterative Differentiation Methods for Bi-Level Optimization Problems |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Heterogeneous graph adaptive flow network |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| HiFE: Hierarchical Feature Ensemble Framework for Few-shot Hypotheses Adaptation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Hierarchical Neural Simulation-Based Inference Over Event Ensembles |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Hierarchical VAE with a Diffusion-based VampPrior |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Hierarchically branched diffusion models leverage dataset structure for class-conditional generation |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| High-dimensional Bayesian Optimization via Covariance Matrix Adaptation Strategy |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Holistic Molecular Representation Learning via Multi-view Fragmentation |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Homogenizing Non-IID Datasets via In-Distribution Knowledge Distillation for Decentralized Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| How Far Are We From AGI: Are LLMs All We Need? |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| How Much Pre-training Is Enough to Discover a Good Subnetwork? |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| How does over-squashing affect the power of GNNs? |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| How good is Good-Turing for Markov samples? |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| How to choose the right transfer learning protocol? A qualitative analysis in a controlled set-up |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| How to think step-by-step: A mechanistic understanding of chain-of-thought reasoning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Hybrid Active Learning with Uncertainty-Weighted Embeddings |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Hybrid Federated Learning for Feature & Sample Heterogeneity: Algorithms and Implementation |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Hyper-parameter Tuning for Fair Classification without Sensitive Attribute Access |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Hyperbolic Random Forests |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Hyperspherical Prototype Node Clustering |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
3 |
| IM-Context: In-Context Learning for Imbalanced Regression Tasks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| IMEX-Reg: Implicit-Explicit Regularization in the Function Space for Continual Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| IMProv: Inpainting-based Multimodal Prompting for Computer Vision Tasks |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| INSPIRE: Incorporating Diverse Feature Preferences in Recourse |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| IRWE: Inductive Random Walk for Joint Inference of Identity and Position Network Embedding |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| ITEM: Improving Training and Evaluation of Message-Passing based GNNs for top-k recommendation |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Identifiable Causal Inference with Noisy Treatment and No Side Information |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Identify Ambiguous Tasks Combining Crowdsourced Labels by Weighting Areas Under the Margin |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Identifying and Clustering Counter Relationships of Team Compositions in PvP Games for Efficient Balance Analysis |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Image Reconstruction via Deep Image Prior Subspaces |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Implicit Neural Representations for Robust Joint Sparse-View CT Reconstruction |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Implicit Regularization of AdaDelta |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Improve Certified Training with Signal-to-Noise Ratio Loss to Decrease Neuron Variance and Increase Neuron Stability |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Improved Convergence of Score-Based Diffusion Models via Prediction-Correction |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Improved Regret Bounds for Linear Adversarial MDPs via Linear Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Improved Variational Bayesian Phylogenetic Inference using Mixtures |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Improved motif-scaffolding with SE(3) flow matching |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Improving Black-box Robustness with In-Context Rewriting |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Improving Diffusion Models for Scene Text Editing with Dual Encoders |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Improving Generalization of Complex Models under Unbounded Loss Using PAC-Bayes Bounds |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Improving Predictor Reliability with Selective Recalibration |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Improving Robust Generalization with Diverging Spanned Latent Space |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Improving Subgraph-GNNs via Edge-Level Ego-Network Encodings |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Improving Text-to-Image Consistency via Automatic Prompt Optimization |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
5 |
| Improving Variational Autoencoder Estimation from Incomplete Data with Mixture Variational Families |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Improving and generalizing flow-based generative models with minibatch optimal transport |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| In-context Learning with Retrieved Demonstrations for Language Models: A Survey |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| InPars-Light: Cost-Effective Unsupervised Training of Efficient Rankers |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Incorporating Inductive Biases to Energy-based Generative Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Incorporating Prior Knowledge into Neural Networks through an Implicit Composite Kernel |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Incorporating Unlabelled Data into Bayesian Neural Networks |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Incremental Extractive Opinion Summarization Using Cover Trees |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Incremental Spatial and Spectral Learning of Neural Operators for Solving Large-Scale PDEs |
✅ |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
5 |
| Independence Testing for Temporal Data |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Indexed Minimum Empirical Divergence-Based Algorithms for Linear Bandits |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| InduCE: Inductive Counterfactual Explanations for Graph Neural Networks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Inductive Global and Local Manifold Approximation and Projection |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Inference from Real-World Sparse Measurements |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| InfoNCE is variational inference in a recognition parameterised model |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Input Normalized Stochastic Gradient Descent Training for Deep Neural Networks |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Integrated Variational Fourier Features for Fast Spatial Modelling with Gaussian Processes |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
4 |
| Internal-Coordinate Density Modelling of Protein Structure: Covariance Matters |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Interpretable Additive Tabular Transformer Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Interpreting CLIP: Insights on the Robustness to ImageNet Distribution Shifts |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Interpreting Global Perturbation Robustness of Image Models using Axiomatic Spectral Importance Decomposition |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Intriguing Properties of Hyperbolic Embeddings in Vision-Language Models |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Introducing "Forecast Utterance" for Conversational Data Science |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Introspective Experience Replay: Look Back When Surprised |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Invariance & Causal Representation Learning: Prospects and Limitations |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| InvariantStock: Learning Invariant Features for Mastering the Shifting Market |
✅ |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
5 |
| Inverse Kernel Decomposition |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Is Value Functions Estimation with Classification Plug-and- play for Offline Reinforcement Learning? |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Jigsaw Game: Federated Clustering |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| KD-BIRL: Kernel Density Bayesian Inverse Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
✅ |
3 |
| Kernel Normalized Convolutional Networks |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Knowledge Accumulation in Continually Learned Representations and the Issue of Feature Forgetting |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Koopman Spectrum Nonlinear Regulators and Efficient Online Learning |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| LEA: Learning Latent Embedding Alignment Model for fMRI Decoding and Encoding |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| LINOCS: Lookahead Inference of Networked Operators for Continuous Stability |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| LInK: Learning Joint Representations of Design and Performance Spaces through Contrastive Learning for Mechanism Synthesis |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| LLMs and the Abstraction and Reasoning Corpus: Successes, Failures, and the Importance of Object-based Representations |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Language Models Are Better Than Humans at Next-token Prediction |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Language Models Speed Up Local Search for Finding Programmatic Policies |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Large Language Models (LLMs) on Tabular Data: Prediction, Generation, and Understanding - A Survey |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Large Language Models Synergize with Automated Machine Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Large Language Models can be Guided to Evade AI-generated Text Detection |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Large-width asymptotics and training dynamics of $\alpha$-Stable ReLU neural networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Layer-diverse Negative Sampling for Graph Neural Networks |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Layerwise complexity-matched learning yields an improved model of cortical area V2 |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| LeOCLR: Leveraging Original Images for Contrastive Learning of Visual Representations |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| LeanVec: Searching vectors faster by making them fit |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learned feature representations are biased by complexity, learning order, position, and more |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
3 |
| Learning $k$-Level Structured Sparse Neural Networks Using Group Envelope Regularization |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning Counterfactually Invariant Predictors |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning Hierarchical Relational Representations through Relational Convolutions |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning Hybrid Interpretable Models: Theory, Taxonomy, and Methods |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Learning Network Granger causality using Graph Prior Knowledge |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning Sparse Graphs for Functional Regression using Graph-induced Operator-valued Kernels |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Learning State Reachability as a Graph in Translation Invariant Goal-based Reinforcement Learning Tasks |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning Sub-Second Routing Optimization in Computer Networks requires Packet-Level Dynamics |
❌ |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
4 |
| Learning Tree-Structured Composition of Data Augmentation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Learning Unlabeled Clients Divergence for Federated Semi-Supervised Learning via Anchor Model Aggregation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning a Decision Tree Algorithm with Transformers |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning by Self-Explaining |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learning from Natural Language Feedback |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Learning multi-modal generative models with permutation-invariant encoders and tighter variational objectives |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Learning the essential in less than 2k additional weights - a simple approach to improve image classification stability under corruptions |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Learning to Abstain From Uninformative Data |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Learning under Imitative Strategic Behavior with Unforeseeable Outcomes |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning-Based Link Anomaly Detection in Continuous-Time Dynamic Graphs |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Let There be Direction in Hypergraph Neural Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Leveraging Endo- and Exo-Temporal Regularization for Black-box Video Domain Adaptation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Leveraging Function Space Aggregation for Federated Learning at Scale |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Leveraging Task Structures for Improved Identifiability in Neural Network Representations |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Linear Bandits with Memory |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Linear Weight Interpolation Leads to Transient Performance Gains |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| LoRA Learns Less and Forgets Less |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Locally Adaptive Federated Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Lookahead Counterfactual Fairness |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Low-Rank Tensor-Network Encodings for Video-to-Action Behavioral Cloning |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Lyra: Orchestrating Dual Correction in Automated Theorem Proving |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| M$^3$PL: Identifying and Exploiting View Bias of Prompt Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| MAGDiff: Covariate Data Set Shift Detection via Activation Graphs of Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| MC Layer Normalization for calibrated uncertainty in Deep Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| MDP: A Generalized Framework for Text-Guided Image Editing by Manipulating the Diffusion Path |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| MESSY Estimation: Maximum-Entropy based Stochastic and Symbolic densitY Estimation |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| MMD-Regularized Unbalanced Optimal Transport |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| MOCA: Self-supervised Representation Learning by Predicting Masked Online Codebook Assignments |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| MUBen: Benchmarking the Uncertainty of Molecular Representation Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Making Translators Privacy-aware on the User's Side |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Manifold Contrastive Learning with Variational Lie Group Operators |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Mantis: Interleaved Multi-Image Instruction Tuning |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| MaskBit: Embedding-free Image Generation via Bit Tokens |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| MaskMA: Towards Zero-Shot Multi-Agent Decision Making with Mask-Based Collaborative Learning |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| MaskOCR: Scene Text Recognition with Masked Vision-Language Pre-training |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Masked Autoencoders are PDE Learners |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Masked multi-prediction for multi-aspect anomaly detection |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Maximizing Global Model Appeal in Federated Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Measuring Orthogonality in Representations of Generative Models |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Mechanistic Interpretability for AI Safety - A Review |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Membership Inference Attacks and Privacy in Topic Modeling |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Memorisation in Machine Learning: A Survey of Results |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Merging Text Transformer Models from Different Initializations |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Merging by Matching Models in Task Parameter Subspaces |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Meta Learning for Support Recovery of High-Dimensional Ising Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Meta-Learning Approach for Joint Multimodal Signals with Multimodal Iterative Adaptation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Meta-Learning under Task Shift |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Mildly Constrained Evaluation Policy for Offline Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Mildly Overparameterized ReLU Networks Have a Favorable Loss Landscape |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Mind the truncation gap: challenges of learning on dynamic graphs with recurrent architectures |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Mini-Batch Optimization of Contrastive Loss |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Mislabeled examples detection viewed as probing machine learning models: concepts, survey and extensive benchmark |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Misspecification-robust Sequential Neural Likelihood for Simulation-based Inference |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Mitigating Group Bias in Federated Learning: Beyond Local Fairness |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Mitigating Off-Policy Bias in Actor-Critic Methods with One-Step Q-learning: A Novel Correction Approach |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Mitigating Simplicity Bias in Deep Learning for Improved OOD Generalization and Robustness |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Mixed Nash for Robust Federated Learning |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| MixedNUTS: Training-Free Accuracy-Robustness Balance via Nonlinearly Mixed Classifiers |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Mixture of Latent Experts Using Tensor Products |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| MoCaE: Mixture of Calibrated Experts Significantly Improves Object Detection |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| MoMA: Model-based Mirror Ascent for Offline Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Modeling Causal Mechanisms with Diffusion Models for Interventional and Counterfactual Queries |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Models of human preference for learning reward functions |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| ModuLoRA: Finetuning 2-Bit LLMs on Consumer GPUs by Integrating with Modular Quantizers |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Modular Federated Contrastive Learning with Twin Normalization for Resource-limited Clients |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Modular Quantization-Aware Training for 6D Object Pose Estimation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Momentum-Based Policy Gradient with Second-Order Information |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| More Agents Is All You Need |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Multi-Accurate CATE is Robust to Unknown Covariate Shifts |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
6 |
| Multi-Fidelity Active Learning with GFlowNets |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
6 |
| Multi-Grid Tensorized Fourier Neural Operator for High- Resolution PDEs |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Multi-Horizon Representations with Hierarchical Forward Models for Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Multi-LoRA Composition for Image Generation |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Multi-conditioned Graph Diffusion for Neural Architecture Search |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Multi-intention Inverse Q-learning for Interpretable Behavior Representation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Multimodal Chain-of-Thought Reasoning in Language Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Multiple Kronecker RLS fusion-based link propagation for drug-side effect prediction |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Multitask Learning Can Improve Worst-Group Outcomes |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Navigating Noise: A Study of How Noise Influences Generalisation and Calibration of Neural Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Neural Circuit Diagrams: Robust Diagrams for the Communication, Implementation, and Analysis of Deep Learning Architectures |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Neural Clamping: Joint Input Perturbation and Temperature Scaling for Neural Network Calibration |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Neural Graph Reasoning: A Survey on Complex Logical Query Answering |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Neural Implicit Manifold Learning for Topology-Aware Density Estimation |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Neural Likelihood Approximation for Integer Valued Time Series Data |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
5 |
| Neural Task Synthesis for Visual Programming |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Neural incomplete factorization: learning preconditioners for the conjugate gradient method |
✅ |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
5 |
| Neural networks can be FLOP-efficient integrators of 1D oscillatory integrands |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| New Evaluation Metrics Capture Quality Degradation due to LLM Watermarking |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| New Guarantees for Learning Revenue Maximizing Menus of Lotteries and Two-Part Tariffs |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| No Identity, no problem: Motion through detection for people tracking |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Node-Specific Space Selection via Localized Geometric Hyperbolicity in Graph Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Noise Stability Optimization for Finding Flat Minima: A Hessian-based Regularization Approach |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Non-Stationary Dueling Bandits Under a Weighted Borda Criterion |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Non-Uniform Smoothness for Gradient Descent |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Non-backtracking Graph Neural Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Nonlinear Behaviour of Critical Points for a Simple Neural Network |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| NorMatch: Matching Normalizing Flows with Discriminative Classifiers for Semi-Supervised Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Normed Spaces for Graph Embedding |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| NuTime: Numerically Multi-Scaled Embedding for Large- Scale Time-Series Pretraining |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Nuisances via Negativa: Adjusting for Spurious Correlations via Data Augmentation |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Object-Centric Relational Representations for Image Generation |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Offline Deep Reinforcement Learning for Visual Distractions via Domain Adversarial Training |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Offline Reinforcement Learning via Tsallis Regularization |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| OmniPred: Language Models as Universal Regressors |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| On Good Practices for Task-Specific Distillation of Large Pretrained Visual Models |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| On Intriguing Layer-Wise Properties of Robust Overfitting in Adversarial Training |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| On Safety in Safe Bayesian Optimization |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Adversarial Robustness of Camera-based 3D Object Detection |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| On the Choice of Learning Rate for Local SGD |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| On the Data Heterogeneity in Adaptive Federated Learning |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| On the Dual Problem of Convexified Convolutional Neural Networks |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Equivalence of Graph Convolution and Mixup |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| On the Importance of Uncertainty in Decision-Making with Large Language Models |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| On the Inherent Privacy Properties of Discrete Denoising Diffusion Models |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| On the Interdependence between Data Selection and Architecture Optimization in Deep Active Learning |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| On the Optimization and Generalization of Multi-head Attention |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| On the Out-of-Distribution Coverage of Combining Split Conformal Prediction and Bayesian Deep Learning |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| On the Reproducibility of: "Learning Perturbations to Explain Time Series Predictions" |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| On the Robustness of Neural Collapse and the Neural Collapse of Robustness |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| On the Stochastic (Variance-Reduced) Proximal Gradient Method for Regularized Expected Reward Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| On the Unreasonable Effectiveness of Federated Averaging with Heterogeneous Data |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| On the numerical reliability of nonsmooth autodiff: a MaxPool case study |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| On the theoretical limit of gradient descent for Simple Recurrent Neural Networks with finite precision |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| One by One, Continual Coordinating with Humans via Hyper-Teammate Identification |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Online Continual Learning via Logit Adjusted Softmax |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Online Continuous Hyperparameter Optimization for Generalized Linear Contextual Bandits |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Online Reference Tracking For Linear Systems with Unknown Dynamics and Unknown Disturbances |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Online Tensor Max-Norm Regularization via Stochastic Optimization |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Oops, I Sampled it Again: Reinterpreting Confidence Intervals in Few-Shot Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Optical Transformers |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Optimal Inference in Contextual Stochastic Block Models |
✅ |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
5 |
| Optimal Transport Perturbations for Safe Reinforcement Learning with Robustness Guarantees |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Optimization with Access to Auxiliary Information |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Optimized Tradeoffs for Private Prediction with Majority Ensembling |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Orthogonal Random Features: Explicit Forms and Sharp Inequalities |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Out-of-Distribution Optimality of Invariant Risk Minimization |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Overcoming Order in Autoregressive Graph Generation for Molecule Generation |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Overcoming the Stability Gap in Continual Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| PASS: Pruning Attention Heads with Almost-sure Sparsity Targets |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| PCNN: Probable-Class Nearest-Neighbor Explanations Improve Fine-Grained Image Classification Accuracy for AIs and Humans |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| PID Control-Based Self-Healing to Improve the Robustness of Large Language Models |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| PLUM: Improving Inference Efficiency By Leveraging Repetition-Sparsity Trade-Off |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| PNeRV: A Polynomial Neural Representation for Videos |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| PRD: Peer Rank and Discussion Improve Large Language Model based Evaluations |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| PaDPaF: Partial Disentanglement with Partially-Federated GANs |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Path Development Network with Finite-dimensional Lie Group |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Pathologies of Predictive Diversity in Deep Ensembles |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| PerSEval: Assessing Personalization in Text Summarizers |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Perception Stitching: Zero-Shot Perception Encoder Transfer for Visuomotor Robot Policies |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Perceptual Similarity for Measuring Decision-Making Style and Policy Diversity in Games |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Persistent Local Homology in Graph Learning |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Persona-aware Generative Model for Code-mixed Language |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Personalised Federated Learning On Heterogeneous Feature Spaces |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Personalized Algorithmic Recourse with Preference Elicitation |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Personalized Federated Learning with Spurious Features: An Adversarial Approach |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Physical Reasoning and Object Planning for Household Embodied Agents |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Physics Informed Distillation for Diffusion Models |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Piecewise-Stationary Dueling Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| PixMIM: Rethinking Pixel Reconstruction in Masked Image Modeling |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Planning with Consistency Models for Model-Based Offline Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Plug, Play, and Generalize: Length Extrapolation with Pointer-Augmented Neural Memory |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Policy Gradient with Kernel Quadrature |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| PopulAtion Parameter Averaging (PAPA) |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Population Priors for Matrix Factorization |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Positional Encoding Helps Recurrent Neural Networks Handle a Large Vocabulary |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Practical Synthesis of Mixed-Tailed Data with Normalizing Flows |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Pre-trained Hypergraph Convolutional Neural Networks with Self-supervised Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Preconditioned Neural Posterior Estimation for Likelihood-free Inference |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Predicting the Encoding Error of SIRENs |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Predictive Pipelined Decoding: A Compute-Latency Trade-off for Exact LLM Decoding |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Pretrained deep models outperform GBDTs in Learning-To-Rank under label scarcity |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Pretraining a Neural Operator in Lower Dimensions |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| PriViT: Vision Transformers for Private Inference |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Prioritized Federated Learning: Leveraging Non-Priority Clients for Targeted Model Improvement |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Prismer: A Vision-Language Model with Multi-Task Experts |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Privacy Preserving Reinforcement Learning for Population Processes |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Privacy-Preserving Split Learning with Vision Transformers using Patch-Wise Random and Noisy CutMix |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| ProFeAT: Projected Feature Adversarial Training for Self-Supervised Learning of Robust Representations |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Probabilistic Matching of Real and Generated Data Statistics in Generative Adversarial Networks |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Promoting Exploration in Memory-Augmented Adam using Critical Momenta |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Prototypical Self-Explainable Models Without Re-training |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Provable Guarantees for Sparsity Recovery with Deterministic Missing Data Patterns |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Provable Membership Inference Privacy |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Proximal Mean Field Learning in Shallow Neural Networks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Pseudo-Differential Neural Operator: Generalize Fourier Neural operator for Learning Solution Operators of Partial Differential Equations |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Pull-back Geometry of Persistent Homology Encodings |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Pushing the Limits of Gradient Descent for Efficient Learning on Large Images |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Q-Learning for Stochastic Control under General Information Structures and Non-Markovian Environments |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| QDC: Quantum Diffusion Convolution Kernels on Graphs |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
5 |
| Quantization Variation: A New Perspective on Training Transformers with Low-Bit Precision |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| RLHF Workflow: From Reward Modeling to Online RLHF |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Re-Thinking Inverse Graphics With Large Language Models |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Read Between the Layers: Leveraging Multi-Layer Representations for Rehearsal-Free Continual Learning with Pre-Trained Models |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Recent Link Classification on Temporal Graphs Using Graph Profiler |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
4 |
| Reconciling Kaplan and Chinchilla Scaling Laws |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Recovering Exact Support in Federated lasso without Optimization |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Recurrent Inertial Graph-Based Estimator (RING): A Single Pluripotent Inertial Motion Tracking Solution |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| RedMotion: Motion Prediction via Redundancy Reduction |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Reducing Variance in Meta-Learning via Laplace Approximation for Regression Tasks |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Regret Bounds for Noise-Free Cascaded Kernelized Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Regularized Proportional Fairness Mechanism for Resource Allocation Without Money |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Reinforcement Learning for Node Selection in Branch-and-Bound |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Repositioning the Subject within Image |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Representation Learning Dynamics of Self-Supervised Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Representation Norm Amplification for Out-of-Distribution Detection in Long-Tail Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Reproducibility Study Of Learning Fair Graph Representations Via Automated Data Augmentations |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Reproducibility Study of "Explaining RL Decisions with Trajectories" |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Reproducibility Study of "ITI-GEN: Inclusive Text-to-Image Generation" |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Reproducibility Study of "Languange-Image COnsistency" |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Reproducibility Study of "Learning Perturbations to Explain Time Series Predictions" |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Reproducibility Study on Adversarial Attacks Against Robust Transformer Trackers |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Reproducibility Study: Equal Improvability: A New Fairness Notion Considering the Long-Term Impact |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Reproducibility and Geometric Intrinsic Dimensionality: An Investigation on Graph Neural Network Research. |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Reproducibility study of "Robust Fair Clustering: A Novel Fairness Attack and Defense Framework" |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Reproducibility study of FairAC |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Reproducibility study of “LICO: Explainable Models with Language-Image Consistency" |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Restricted Random Pruning at Initialization for High Compression Range |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Rethinking Teacher-Student Curriculum Learning through the Cooperative Mechanics of Experience |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Revealing an Overlooked Challenge in Class-Incremental Graph Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Revisiting Active Learning in the Era of Vision Foundation Models |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Revisiting Deep Feature Reconstruction for Logical and Structural Industrial Anomaly Detection |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Revisiting Discrete Soft Actor-Critic |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Revisiting Energy Based Models as Policies: Ranking Noise Contrastive Estimation and Interpolating Energy Models |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Revisiting Feature Prediction for Learning Visual Representations from Video |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Revisiting Generalized p-Laplacian Regularized Framelet GCNs: Convergence, Energy Dynamic and as Non-Linear Diffusion |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Revisiting Non-separable Binary Classification and its Applications in Anomaly Detection |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Revisiting Random Weight Perturbation for Efficiently Improving Generalization |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Revisiting stochastic submodular maximization with cardinality constraint: A bandit perspective |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Reward Guided Latent Consistency Distillation |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Reward Poisoning on Federated Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Risk Bounds for Mixture Density Estimation on Compact Domains via the h-Lifted Kullback–Leibler Divergence |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Risk-Controlling Model Selection via Guided Bayesian Optimization |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| RoboCat: A Self-Improving Generalist Agent for Robotic Manipulation |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Robust Distortion-free Watermarks for Language Models |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Robust Feature Inference: A Test-time Defense Strategy using Spectral Projections |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Robust Guided Diffusion for Offline Black-Box Optimization |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Robust Learning Rate Selection for Stochastic Optimization via Splitting Diagnostic |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Robust Stochastic Optimization via Gradient Quantile Clipping |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Robust and Efficient Quantization-aware Training via Coreset Selection |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Rotate the ReLU to Sparsify Deep Networks Implicitly |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Routers in Vision Mixture of Experts: An Empirical Study |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| SA-MLP: Distilling Graph Knowledge from GNNs into Structure-Aware MLP |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| SASSL: Enhancing Self-Supervised Learning via Neural Style Transfer |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| SEAL: Simultaneous Label Hierarchy Exploration And Learning |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| SPriFed-OMP: A Differentially Private Federated Learning Algorithm for Sparse Basis Recovery |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| SQL-PaLM: Improved large language model adaptation for Text-to-SQL |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Scalable Hierarchical Self-Attention with Learnable Hierarchy for Long-Range Interactions |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Scale Equalization for Multi-Level Feature Fusion |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Scaling (Down) CLIP: A Comprehensive Analysis of Data,Architecture, and Training Strategies |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Scaling Laws for Imitation Learning in Single-Agent Games |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Scaling Up Bayesian Neural Networks with Neural Networks |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Scaling Vision-and-Language Navigation With Offline RL |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Score-Based Multimodal Autoencoder |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Score-based Explainability for Graph Representations |
✅ |
❌ |
✅ |
✅ |
✅ |
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5 |
| Selective Classification Under Distribution Shifts |
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❌ |
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5 |
| Selective Pre-training for Private Fine-tuning |
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✅ |
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4 |
| Self-Improvement for Neural Combinatorial Optimization: Sample Without Replacement, but Improvement |
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❌ |
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6 |
| Self-Supervised Visual Representation Learning for Medical Image Analysis: A Comprehensive Survey |
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✅ |
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❌ |
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2 |
| Self-supervised Color Generalization in Reinforcement Learning |
❌ |
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❌ |
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5 |
| SelfXit: An Unsupervised Early Exit Mechanism for Deep Neural Networks |
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7 |
| Semantic Positive Pairs for Enhancing Visual Representation Learning of Instance Discrimination Methods |
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5 |
| Semantic similarity prediction is better than other semantic similarity measures |
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❌ |
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5 |
| Semi-Supervised Semantic Segmentation via Marginal Contextual Information |
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❌ |
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6 |
| Sensitivity-Aware Amortized Bayesian Inference |
❌ |
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❌ |
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5 |
| Separability Analysis for Causal Discovery in Mixture of DAGs |
✅ |
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❌ |
❌ |
✅ |
2 |
| Separable Operator Networks |
❌ |
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❌ |
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4 |
| SeqLink: A Robust Neural-ODE Architecture for Modelling Partially Observed Time Series |
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❌ |
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6 |
| Sequential Best-Arm Identification with Application to P300 Speller |
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❌ |
❌ |
❌ |
✅ |
2 |
| Series of Hessian-Vector Products for Tractable Saddle-Free Newton Optimisation of Neural Networks |
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✅ |
✅ |
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7 |
| Set Features for Anomaly Detection |
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❌ |
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6 |
| Sight Beyond Text: Multi-Modal Training Enhances LLMs in Truthfulness and Ethics |
❌ |
✅ |
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❌ |
❌ |
❌ |
3 |
| Simple Drop-in LoRA Conditioning on Attention Layers Will Improve Your Diffusion Model |
❌ |
✅ |
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❌ |
❌ |
❌ |
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3 |
| Simple Imputation Rules for Prediction with Missing Data: Theoretical Guarantees vs. Empirical Performance |
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✅ |
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❌ |
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5 |
| Simple Steps to Success: A Method for Step-Based Counterfactual Explanations |
✅ |
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✅ |
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❌ |
✅ |
5 |
| Simple and Scalable Strategies to Continually Pre-train Large Language Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Simultaneous Dimensionality Reduction: A Data Efficient Approach for Multimodal Representations Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Single Image Test-Time Adaptation for Segmentation |
❌ |
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❌ |
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4 |
| Single-Shot Plug-and-Play Methods for Inverse Problems |
✅ |
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❌ |
✅ |
❌ |
✅ |
4 |
| Size Lowerbounds for Deep Operator Networks |
❌ |
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❌ |
❌ |
❌ |
✅ |
2 |
| Sketch and shift: a robust decoder for compressive clustering |
✅ |
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❌ |
❌ |
❌ |
✅ |
3 |
| Smoothed Robustness Analysis: Bridging worst- and average-case robustness analyses via smoothed analysis |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Soft Merging of Experts with Adaptive Routing |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Solving Inverse Problems with Model Mismatch using Untrained Neural Networks within Model-based Architectures |
❌ |
✅ |
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❌ |
✅ |
❌ |
✅ |
4 |
| Solving Robust MDPs through No-Regret Dynamics |
✅ |
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✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Solving the Tree Containment Problem Using Graph Neural Networks |
❌ |
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✅ |
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❌ |
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5 |
| Sparse Contextual CDF Regression |
❌ |
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❌ |
❌ |
❌ |
✅ |
2 |
| Sparse Modal Regression with Mode-Invariant Skew Noise |
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❌ |
❌ |
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4 |
| Sparsifying Bayesian neural networks with latent binary variables and normalizing flows |
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❌ |
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5 |
| Spectral Self-supervised Feature Selection |
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❌ |
❌ |
❌ |
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3 |
| Spike Accumulation Forwarding for Effective Training of Spiking Neural Networks |
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❌ |
✅ |
❌ |
✅ |
4 |
| SpikeGPT: Generative Pre-trained Language Model with Spiking Neural Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Stability and Generalization in Free Adversarial Training |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Standard-Deviation-Inspired Regularization for Improving Adversarial Robustness |
✅ |
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❌ |
✅ |
❌ |
✅ |
4 |
| State-wise Constrained Policy Optimization |
✅ |
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❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Statistical Component Separation for Targeted Signal Recovery in Noisy Mixtures |
✅ |
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✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Statistical Mechanics of Min-Max Problems |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Statistical and Computational Complexities of BFGS Quasi-Newton Method for Generalized Linear Models |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Stealthy Backdoor Attack via Confidence-driven Sampling |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Stochastic Bandits for Egalitarian Assignment |
✅ |
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❌ |
❌ |
❌ |
✅ |
4 |
| Stochastic Direct Search Methods for Blind Resource Allocation |
✅ |
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❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Stochastic Re-weighted Gradient Descent via Distributionally Robust Optimization |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Strategies for Pretraining Neural Operators |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Strengthening Interpretability: An Investigative Study of Integrated Gradient Methods |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Structural Pruning of Pre-trained Language Models via Neural Architecture Search |
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✅ |
✅ |
❌ |
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6 |
| Structure-Preserving Network Compression Via Low-Rank Induced Training Through Linear Layers Composition |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Supervised Domain Adaptation Based on Marginal and Conditional Distributions Alignment |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Support-Set Context Matters for Bongard Problems |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| SwinGNN: Rethinking Permutation Invariance in Diffusion Models for Graph Generation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Switching Latent Bandits |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Synaptic Interaction Penalty: Appropriate Penalty Term for Energy-Efficient Spiking Neural Networks |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Synthesizing Libraries of Programs with Auxiliary Functions |
✅ |
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✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Synthetic data shuffling accelerates the convergence of federated learning under data heterogeneity |
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✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| TAP: The Attention Patch for Cross-Modal Knowledge Transfer from Unlabeled Modality |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| TIGERScore: Towards Building Explainable Metric for All Text Generation Tasks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| TOTEM: TOkenized Time Series EMbeddings for General Time Series Analysis |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Tabula: Efficiently Computing Nonlinear Activation Functions for Secure Neural Network Inference |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| TacoGFN: Target-conditioned GFlowNet for Structure-based Drug Design |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| Targeted Active Learning for Bayesian Decision-Making |
✅ |
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✅ |
❌ |
❌ |
✅ |
❌ |
3 |
| Task-Relevant Feature Selection with Prediction Focused Mixture Models |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| TeaMs-RL: Teaching LLMs to Generate Better Instruction Datasets via Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
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5 |
| Teacher-Guided Graph Contrastive Learning |
✅ |
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✅ |
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❌ |
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6 |
| Temporal Difference Learning with Compressed Updates: Error-Feedback meets Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Temporally Rich Deep Learning Models for Magnetoencephalography |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| TensorVAE: a simple and efficient generative model for conditional molecular conformation generation |
❌ |
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6 |
| Text Descriptions are Compressive and Invariant Representations for Visual Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| The Cold Posterior Effect Indicates Underfitting, and Cold Posteriors Represent a Fully Bayesian Method to Mitigate It |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| The Cross-entropy of Piecewise Linear Probability Density Functions |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
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3 |
| The Disagreement Problem in Explainable Machine Learning: A Practitioner’s Perspective |
❌ |
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✅ |
✅ |
❌ |
❌ |
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4 |
| The Fair Value of Data Under Heterogeneous Privacy Constraints in Federated Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| The Harmonic Indel Distance |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| The Impact of Syntactic and Semantic Proximity on Machine Translation with Back-Translation |
❌ |
❌ |
❌ |
✅ |
✅ |
❌ |
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3 |
| The Interplay of Uncertainty Modeling and Deep Active Learning: An Empirical Analysis in Image Classification |
❌ |
✅ |
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✅ |
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❌ |
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5 |
| The Kernel Perspective on Dynamic Mode Decomposition |
✅ |
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✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| The Klarna Product Page Dataset: Web Element Nomination with Graph Neural Networks and Large Language Models |
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❌ |
✅ |
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5 |
| The Missing U for Efficient Diffusion Models |
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✅ |
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❌ |
❌ |
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2 |
| The Real Tropical Geometry of Neural Networks for Binary Classification |
❌ |
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❌ |
❌ |
❌ |
❌ |
0 |
| The Responsible Foundation Model Development Cheatsheet: A Review of Tools & Resources |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| The Slingshot Effect: A Late-Stage Optimization Anomaly in Adaptive Gradient Methods |
✅ |
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✅ |
✅ |
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5 |
| The Survival Bandit Problem |
✅ |
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❌ |
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2 |
| The Trifecta: Three simple techniques for training deeper Forward-Forward networks |
✅ |
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✅ |
❌ |
❌ |
✅ |
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5 |
| The Unreasonable Effectiveness of Gaussian Score Approximation for Diffusion Models and its Applications |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
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4 |
| Threshold Moving for Online Class Imbalance Learning with Dynamic Evolutionary Cost Vector |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Time Series Continuous Modeling for Imputation and Forecasting with Implicit Neural Representations |
✅ |
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✅ |
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✅ |
✅ |
✅ |
7 |
| To Transfer or Not to Transfer: Suppressing Concepts from Source Representations |
✅ |
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✅ |
✅ |
✅ |
❌ |
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5 |
| Todyformer: Towards Holistic Dynamic Graph Transformers with Structure-Aware Tokenization |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Toward a Complete Criterion for Value of Information in Insoluble Decision Problems |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Towards Backwards-Compatible Data with Confounded Domain Adaptation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Towards Empirical Interpretation of Internal Circuits and Properties in Grokked Transformers on Modular Polynomials |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
4 |
| Towards Minimal Targeted Updates of Language Models with Targeted Negative Training |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Towards Provable Log Density Policy Gradient |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Towards Size-Independent Generalization Bounds for Deep Operator Nets |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Towards Truly Zero-shot Compositional Visual Reasoning with LLMs as Programmers |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Towards Trustworthy Reranking: A Simple yet Effective Abstention Mechanism |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Towards Unbiased Calibration using Meta-Regularization |
✅ |
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✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Towards Understanding Adversarial Transferability in Federated Learning |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Towards Understanding Dual BN In Hybrid Adversarial Training |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Towards Understanding Variants of Invariant Risk Minimization through the Lens of Calibration |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Towards fully covariant machine learning |
❌ |
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✅ |
❌ |
❌ |
✅ |
3 |
| Towards generalizing deep-audio fake detection networks |
❌ |
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❌ |
✅ |
✅ |
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5 |
| Training Graph Neural Networks Subject to a Tight Lipschitz Constraint |
✅ |
✅ |
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✅ |
✅ |
❌ |
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6 |
| Training LLMs over Neurally Compressed Text |
❌ |
❌ |
✅ |
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❌ |
❌ |
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3 |
| Training-free Graph Neural Networks and the Power of Labels as Features |
❌ |
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✅ |
❌ |
❌ |
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4 |
| Transfer Learning for Bayesian Optimization on Heterogeneous Search Spaces |
✅ |
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✅ |
✅ |
❌ |
❌ |
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5 |
| Transfer Learning for High-dimensional Quantile Regression with Statistical Guarantee |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Transfer Learning with Informative Priors: Simple Baselines Better than Previously Reported |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Transformer Architecture Search for Improving Out-of-Domain Generalization in Machine Translation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Transformer-Based Models Are Not Yet Perfect At Learning to Emulate Structural Recursion |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Tree Ensembles for Contextual Bandits |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
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4 |
| Trusted Aggregation (TAG): Backdoor Defense in Federated Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Tweedie Moment Projected Diffusions for Inverse Problems |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Two Failures of Self-Consistency in the Multi-Step Reasoning of LLMs |
❌ |
❌ |
✅ |
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❌ |
❌ |
✅ |
3 |
| UCB Exploration for Fixed-Budget Bayesian Best Arm Identification |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| UPS: Efficiently Building Foundation Models for PDE Solving via Cross-Modal Adaptation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Uncertainty in Graph Neural Networks: A Survey |
❌ |
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❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Uncovering Sets of Maximum Dissimilarity on Random Process Data |
✅ |
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❌ |
✅ |
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4 |
| Understanding Fairness Surrogate Functions in Algorithmic Fairness |
✅ |
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4 |
| Understanding Smoothness of Vector Gaussian Processes on Product Spaces |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Understanding Sparse Neural Networks from their Topology via Multipartite Graph Representations |
❌ |
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✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Understanding and Improving Transfer Learning of Deep Models via Neural Collapse |
❌ |
❌ |
✅ |
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✅ |
❌ |
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4 |
| Understanding the Role of Invariance in Transfer Learning |
❌ |
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✅ |
❌ |
❌ |
✅ |
4 |
| Understanding the Role of Layer Normalization in Label-Skewed Federated Learning |
❌ |
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✅ |
✅ |
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5 |
| Undetectable Steganography for Language Models |
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3 |
| UniCtrl: Improving the Spatiotemporal Consistency of Text-to-Video Diffusion Models via Training-Free Unified Attention Control |
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5 |
| Unified Convergence Theory of Stochastic and Variance-Reduced Cubic Newton Methods |
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3 |
| Uniformly Distributed Feature Representations for Fair and Robust Learning |
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5 |
| Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Code |
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3 |
| Universal Functional Regression with Neural Operator Flows |
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5 |
| Universal Neurons in GPT2 Language Models |
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3 |
| Unlearning Sensitive Information in Multimodal LLMs: Benchmark and Attack-Defense Evaluation |
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3 |
| Unleashing the Potential of Acquisition Functions in High-Dimensional Bayesian Optimization |
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6 |
| Unleashing the Power of Visual Prompting At the Pixel Level |
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3 |
| Unmasking the Veil: An Investigation into Concept Ablation for Privacy and Copyright Protection in Images |
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2 |
| Unsupervised Discovery of Steerable Factors When Graph Deep Generative Models Are Entangled |
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4 |
| Unsupervised Domain Adaptation by Learning Using Privileged Information |
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6 |
| Unsupervised Training of Convex Regularizers using Maximum Likelihood Estimation |
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3 |
| Unveiling Adversarially Robust Graph Lottery Tickets |
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5 |
| Using Motion Cues to Supervise Single-frame Body Pose & Shape Estimation in Low Data Regimes |
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4 |
| Using Skew to Assess the Quality of GAN-generated Image Features |
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3 |
| Using Sum-Product Networks to Assess Uncertainty in Deep Active Learning |
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4 |
| Variance-aware decision making with linear function approximation under heavy-tailed rewards |
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2 |
| Variational Autoencoding of Dental Point Clouds |
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3 |
| Variational Bayesian Imaging with an Efficient Surrogate Score-based Prior |
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5 |
| Variational Classification: A Probabilistic Generalization of the Softmax Classifier |
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5 |
| Variational Inference on the Final-Layer Output of Neural Networks |
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4 |
| Variational Learning ISTA |
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5 |
| Variational Pseudo Marginal Methods for Jet Reconstruction in Particle Physics |
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3 |
| Variational autoencoder with weighted samples for high-dimensional non-parametric adaptive importance sampling |
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3 |
| Variational excess risk bound for general state space models |
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0 |
| VidEdit: Zero-Shot and Spatially Aware Text-Driven Video Editing |
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3 |
| Video Diffusion Models: A Survey |
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1 |
| VideoGLUE: Video General Understanding Evaluation of Foundation Models |
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4 |
| Vision Learners Meet Web Image-Text Pairs |
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5 |
| Vision-Language Dataset Distillation |
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5 |
| Vision-Language Instruction Tuning: A Review and Analysis |
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4 |
| Vision-and-Language Navigation Today and Tomorrow: A Survey in the Era of Foundation Models |
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2 |
| VisionAD, a software package of performant anomaly detection algorithms, and Proportion Localised, an interpretable metric |
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4 |
| Visual Prompt Based Personalized Federated Learning |
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5 |
| Voyager: An Open-Ended Embodied Agent with Large Language Models |
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4 |
| Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits |
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4 |
| WaveBench: Benchmarking Data-driven Solvers for Linear Wave Propagation PDEs |
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6 |
| Wavelet Networks: Scale-Translation Equivariant Learning From Raw Time-Series |
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6 |
| We're Not Using Videos Effectively: An Updated Domain Adaptive Video Segmentation Baseline |
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4 |
| Weighted L1 and L0 Regularization Using Proximal Operator Splitting Methods |
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7 |
| Weighted Risk Invariance: Domain Generalization under Invariant Feature Shift |
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5 |
| What Does Softmax Probability Tell Us about Classifiers Ranking Across Diverse Test Conditions? |
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3 |
| What Has Been Overlooked in Contrastive Source-Free Domain Adaptation: Leveraging Source-Informed Latent Augmentation within Neighborhood Context |
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6 |
| What do larger image classifiers memorise? |
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3 |
| What is the Solution for State-Adversarial Multi-Agent Reinforcement Learning? |
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7 |
| When Stability meets Sufficiency: Informative Explanations that do not Overwhelm |
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3 |
| When is Momentum Extragradient Optimal? A Polynomial-Based Analysis |
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2 |
| When low-vision task meets dense prediction tasks with less data: an auxiliary self-trained geometry regularization |
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5 |
| Where Did the Gap Go? Reassessing the Long-Range Graph Benchmark |
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4 |
| Why Fine-grained Labels in Pretraining Benefit Generalization? |
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4 |
| Why should autoencoders work? |
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3 |
| World Models via Policy-Guided Trajectory Diffusion |
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5 |
| XAI-Based Detection of Adversarial Attacks on Deepfake Detectors |
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6 |
| XAudit : A Learning-Theoretic Look at Auditing with Explanations |
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4 |
| XPL: A Cross-Model framework for Semi-Supervised Prompt Learning in Vision-Language Models |
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5 |
| Your Classifier Can Be Secretly a Likelihood-Based OOD Detector |
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5 |
| Zero-Order One-Point Gradient Estimate in Consensus-Based Distributed Stochastic Optimization |
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4 |
| ZigZag: Universal Sampling-free Uncertainty Estimation Through Two-Step Inference |
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4 |
| [Re] CUDA: Curriculum of Data Augmentation for Long‐tailed Recognition |
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5 |
| [Re] Classwise-Shapley values for data valuation |
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5 |
| [Re] Explaining Temporal Graph Models through an Explorer-Navigator Framework |
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5 |
| [Re] GNNInterpreter: A probabilistic generative model-level explanation for Graph Neural Networks |
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7 |
| [Re] On the Reproducibility of Post-Hoc Concept Bottleneck Models |
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5 |
| [Re] Reproducibility Study of “Explaining Temporal Graph Models Through an Explorer-Navigator Framework" |
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5 |
| iHyperTime: Interpretable Time Series Generation with Implicit Neural Representations |
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4 |
| kNN-CLIP: Retrieval Enables Training-Free Segmentation on Continually Expanding Large Vocabularies |
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5 |
| “Studying How to Efficiently and Effectively Guide Models with Explanations” - A Reproducibility Study |
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5 |