Temporally Disentangled Representation Learning
Authors: Weiran Yao, Guangyi Chen, Kun Zhang
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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 weiran@cmu.edu Guangyi Chen CMU & MBZUAI guangyichen1994@gmail.com Kun Zhang CMU & MBZUAI kunz1@cmu.edu |
| 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. |