Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Temporally Disentangled Representation Learning
Authors: Weiran Yao, Guangyi Chen, Kun Zhang
NeurIPS 2022 | Venue PDF | LLM Run Details | Input Tokens: 18,503 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,310 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the identifiability results of TDRL on a number of simulated and real-world time-series datasets. We first introduce the evaluation metrics and baselines. (1) Evaluation Metrics. To evaluate the identifiability of the latent variables, we compute Mean Correlation Coefficient (MCC) on the test dataset. [...] (2) Baselines. Nonlinear ICA methods are used: [...] |
| Researcher Affiliation | Academia | CMU EMAIL Guangyi Chen CMU & MBZUAI EMAIL Kun Zhang CMU & MBZUAI EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at: https://github.com/weirayao/tdrl. |
| Open Datasets | Yes | Motion Capture Data CMU-Mocap We experimented with another real-world motion capture dataset (CMU-Mocap). |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | We fit TDRL with two-dimensional change factors dyn r . We set the latent size n = 8 and the lag number L = 2. |