Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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 | Venue PDF | 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. |