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.
A Statistical Analysis of Wasserstein Autoencoders for Intrinsically Low-dimensional Data
Authors: Saptarshi Chakraborty, Peter Bartlett
ICLR 2024 | Venue PDF | LLM Run Details | Input Tokens: 36,819 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,589 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 | To bridge the gap between the theory and practice of WAEs, in this paper, we show that WAEs can learn the data distributions when the network architectures are properly chosen. We show that the convergence rates of the expected excess risk in the number of samples for WAEs are independent of the high feature dimension, instead relying only on the intrinsic dimension of the data distribution. |
| Researcher Affiliation | Collaboration | Saptarshi Chakraborty UC Berkeley EMAIL Peter L. Bartlett Google DeepMind & UC Berkeley EMAIL |
| Pseudocode | No | The paper does not include pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | The codes for this experimental study can be found at https://github.com/Saptarshi C98/WAE. |
| Open Datasets | Yes | We use a pre-trained Bi-directional GAN (Donahue et al., 2017) with 128 latent entries and outputs of size 128 × 128 × 3, trained on the ImageNet dataset (Deng et al., 2009). |
| Dataset Splits | No | We train a WAE model with the standard architecture as proposed by Tolstikhin et al. (2018) with the number of training samples varying in {2000, 4000, . . . , 10000} and keep the last 1000 images for testing. The paper does not mention a validation split. |
| Hardware Specification | No | The paper mentions reducing image sizes "for computational ease" but does not specify any hardware (GPU, CPU models, etc.) used for experiments. |
| Software Dependencies | No | The paper mentions using "Adam optimizer (Kingma & Ba, 2015)" but does not specify any software names with version numbers for reproducibility. |
| Experiment Setup | Yes | For the latent distribution, we use the standard Gaussian distribution on the latent space R8 and use the Adam optimizer (Kingma & Ba, 2015) with a learning rate of 0.0001. We also take λ = 10 for the penalty on the dissimilarity in objective (4). After training for 10 epochs, we generate 1000 sample images from the distribution ˆG ν (see Section 3 for notations) and compute the Frechet Inception Distance (FID) (Heusel et al., 2017) to assess the quality of the generated samples with respect to the target distribution. |