Statistical Guarantees for Variational Autoencoders using PAC-Bayesian Theory
Authors: Sokhna Diarra Mbacke, Florence Clerc, Pascal Germain
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide preliminary experiments on synthetic datasets in the supplementary material. |
| Researcher Affiliation | Academia | Sokhna Diarra Mbacke Université Laval sokhna-diarra.mbacke.1@ulaval.ca Florence Clerc Mc Gill University florence.clerc@mail.mcgill.ca Pascal Germain Université Laval pascal.germain@ift.ulaval.ca |
| Pseudocode | No | The paper does not contain any sections explicitly labeled "Pseudocode" or "Algorithm", nor does it present any structured algorithmic steps in a code-like format. |
| Open Source Code | No | The paper mentions "preliminary experiments on synthetic datasets in the supplementary material" in the conclusion, which implies some code might exist, but it does not provide an explicit statement about releasing the source code for the methodology or a link to a code repository. |
| Open Datasets | No | The paper mentions "synthetic datasets" in the conclusion but does not provide any specific names of publicly available datasets, citations, links, or repository information for accessing them. |
| Dataset Splits | No | The paper does not provide specific details regarding dataset splits, such as percentages for training, validation, or testing sets, nor does it refer to standard predefined splits with citations. |
| Hardware Specification | No | The paper does not specify any hardware used for running experiments, such as specific GPU models, CPU types, or memory configurations. |
| Software Dependencies | No | The paper does not provide specific version numbers for any ancillary software components or libraries used in the experiments. |
| Experiment Setup | No | The paper does not provide specific details about the experimental setup, such as hyperparameter values (e.g., learning rate, batch size, number of epochs) or optimizer settings. |