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..
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. |