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.
Isolating Sources of Disentanglement in Variational Autoencoders
Authors: Ricky T. Q. Chen, Xuechen Li, Roger B. Grosse, David K. Duvenaud
NeurIPS 2018 | Venue PDF | LLM Run Details | Input Tokens: 15,441 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,298 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 perform extensive quantitative and qualitative experiments, in both restricted and non-restricted settings, and show a strong relation between total correlation and disentanglement, when the model is trained using our framework. |
| Researcher Affiliation | Academia | Ricky T. Q. Chen, Xuechen Li, Roger Grosse, David Duvenaud University of Toronto, Vector Institute |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at . |
| Open Datasets | Yes | We perform quantitative evaluations with two datasets, a dataset of 2D shapes [39] and a dataset of synthetic 3D faces [40]... [39] refers to: dsprites: Disentanglement testing sprites dataset. https://github.com/deepmind/dsprites-dataset/, 2017. |
| Dataset Splits | No | The paper mentions using datasets for experiments but does not provide specific details on how these datasets were split into training, validation, or test sets (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU models, CPU types, memory) used to conduct the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python 3.x, TensorFlow 1.x, PyTorch 1.x) that were used for the experiments. |
| Experiment Setup | Yes | We used β = 4 for β-VAE and β = 6 for β-TCVAE, based on modes in Figure 2. For Info GAN, we used 5 continuous latent codes and 5 noise variables. Other settings are chosen following those suggested by [6], but we also added instance noise [41] to stabilize training. ... we tuned β [1, 80] and used double the number of iterations for Factor VAE. Note that while β-VAE, Factor VAE and β-TCVAE use a fully connected architecture for the d Sprites dataset, Info GAN uses a convolutional architecture for increased stability. |