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 [1].
Contrastively Disentangled Sequential Variational Autoencoder
Authors: Junwen Bai, Weiran Wang, Carla P. Gomes
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show that C-DSVAE significantly outperforms the previous state-of-the-art methods on multiple metrics. |
| Researcher Affiliation | Collaboration | Junwen Bai Cornell University EMAIL Weiran Wang Google EMAIL Carla Gomes Cornell University EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described, nor does it explicitly state that the code is publicly available. |
| Open Datasets | Yes | Sprites [51] is a cartoon character video dataset. MUG [52] is a facial expression video dataset. SM-MNIST ([53], Stochastic Moving MNIST) is a dataset... TIMIT [54] is a corpus of read speech... |
| Dataset Splits | Yes | All datasets are separated into training, validation and testing splits following [46, 33, 18]. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory, or cloud instances) used for running the experiments. |
| Software Dependencies | No | The paper mentions software components like LSTMs and the Adam optimizer but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | As is commonly done in VAE learning [20], in (6) we add coefficients α to the KL terms, β to the contrastive terms, while the coefficient γ of I(s; z1:T ) is fixed to be 1. In our experiments, α is tuned over {0.6, 0.9, 1.0, 2.0} and β is tuned over {0.1, 0.2, 0.5, 0.7, 1.0, 2.0, 5.0}. We use the Adam optimizer [57] with the learning rate chosen from {0.0005, 0.001, 0.0015, 0.002} through grid search. |