Temporally Disentangled Representation Learning under Unknown Nonstationarity
Authors: Xiangchen Song, Weiran Yao, Yewen Fan, Xinshuai Dong, Guangyi Chen, Juan Carlos Niebles, Eric Xing, Kun Zhang
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical evaluations demonstrated the reliable identification of time-delayed latent causal influences, with our methodology substantially outperforming existing baselines that fail to exploit the nonstationarity adequately and then, consequently, cannot distinguish distribution shifts. |
| Researcher Affiliation | Collaboration | Xiangchen Song1 Weiran Yao2 Yewen Fan1 Xinshuai Dong1 Guangyi Chen1,3 Juan Carlos Niebles2 Eric Xing1,3 Kun Zhang1,3 1Carnegie Mellon University 2Salesforce Research 3Mohamed bin Zayed University of Artificial Intelligence |
| Pseudocode | No | The paper describes its model architecture and optimization process but does not include any explicitly labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | Yes | The code can be found via https://github.com/xiangchensong/nctrl. |
| Open Datasets | Yes | Video data Mo Seq Dataset We test NCTRL framework to analyze mouse behavior video data from Wiltschko et al. [19]... Dataset can be accessed via https://dattalab.github.io/moseq2-website/index.html |
| Dataset Splits | No | The paper implicitly uses a training process for its model, but it does not provide specific details on validation splits, such as percentages, sample counts, or explicit references to predefined validation sets. |
| Hardware Specification | Yes | All experiments are done in a GPU workstation with CPU: Intel i7-13700K, GPU: NVIDIA RTX 4090, Memory: 128 GB. |
| Software Dependencies | No | The paper details network architectures and components like Conv2D and Leaky ReLU but does not list specific software dependencies with version numbers (e.g., PyTorch version, Python version, specific library versions). |
| Experiment Setup | No | The paper discusses the overall model architecture and optimization objectives but does not provide specific details on hyperparameters (e.g., learning rate, batch size) or other system-level training settings in the main text. |