Towards Robust Deterministic and Probabilistic Modeling for Predictive Learning
Authors: Xuesong Nie, Haoyuan Jin, Vijayakumar Bhagavatula, Xiaofeng Liu
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate the superiority of DDP across diverse scenario evaluations. ... Extensive experiments show that DDP achieves state-of-the-art performance across various real-world scenes. ... We demonstrate the effectiveness of the DDP model with multi-scenario evaluations. ... In this section, we further perform extensive ablation studies to study the components effectiveness in our DDP. |
| Researcher Affiliation | Academia | Xuesong Nie1,2 , Haoyuan Jin1 , Vijayakumar Bhagavatula3 and Xiaofeng Liu2, 1Zhejiang University 2Yale University 3Carnegie Mellon University EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes methods and algorithms using mathematical equations and textual explanations, but it does not contain a clearly labeled "Pseudocode" or "Algorithm" block, nor does it present structured steps in a code-like format. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code or provide any links to a code repository. |
| Open Datasets | Yes | 4.1 Human Motions: UCF Sports Dataset and Setup. UCF Sports [Rodriguez et al., 2008] ... 4.2 Synthetic Motions: Moving MNIST Dataset and Setup. The Moving MNIST [Srivastava et al., 2015] dataset is constructed ... 4.3 Driving Scenes: KITTI&Caltech Dataset and Setup. The KITTI&Caltech [Geiger et al., 2013; Doll ar et al., 2009] dataset ... 4.4 Traffic Flow: Taxi BJ Dataset and Setup. Taxi BJ [Zhang and others, 2017] comprises taxi GPS trajectory data ... 4.5 Global Climate: Weather Bench Dataset and Setup. Weather Bench [Rasp et al., 2020] contains climatic data ... |
| Dataset Splits | Yes | UCF Sports ... using 6,288 sequences for training and 752 for testing. ... Moving MNIST ... There are 10,000 sequences for training and 10,000 for testing. ... KITTI&Caltech ... we train the model on the KITTI [Geiger et al., 2013] dataset and evaluate it against the Caltech Pedestrian [Doll ar et al., 2009] dataset. ... Weather Bench ... using 2010-2015 for training, 2016 for validation, and 2017-2018 for testing. |
| Hardware Specification | Yes | Our method uses Py Torch on an NVIDIA A100 GPU |
| Software Dependencies | No | Our method uses Py Torch on an NVIDIA A100 GPU, training with 16-sequence minibatches, the Adam optimizer, and the One Cycle scheduler. While PyTorch is mentioned, a specific version number is not provided, nor are versions for other software components. |
| Experiment Setup | Yes | Implementation Details. Our method uses Py Torch on an NVIDIA A100 GPU, training with 16-sequence minibatches, the Adam optimizer, and the One Cycle scheduler. We apply a weight decay of 5e 2 and select learning rates from {1e 2, 5e 3, 1e 3} for stability. We use the MSE loss to supervise training and stochastic depth for regularization. |