AdaVAE: Bayesian Structural Adaptation for Variational Autoencoders
Authors: Paribesh Regmi, Rui Li
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show that the inference framework effectively prevents overfitting in both shallow and deep VAE models, yielding state-of-the-art performance. |
| Researcher Affiliation | Academia | Paribesh Regmi Rui Li Rochester Institute of Technology {pr8537, rxlics}@rit.edu |
| Pseudocode | No | The paper describes the method mathematically but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Implementation details are in the Appendix. Codes are provided. |
| Open Datasets | Yes | We assess our estimator using three benchmark datasets: MNIST [47], Omniglot [48], and Caltech101 Silhouettes [49]. |
| Dataset Splits | No | The paper mentions using benchmark datasets but does not provide specific details on the train/validation/test splits, such as percentages, sample counts, or explicit standard split references for reproduction. |
| Hardware Specification | No | The paper states: 'We acknowledge Research Computing at the Rochester Institute of Technology [57] for providing computational resources.' but does not provide specific hardware details such as GPU/CPU models, memory, or processor types. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies (e.g., programming languages, libraries, or frameworks) used in the experiments. |
| Experiment Setup | Yes | To investigate how network structures evolve during training epochs, we set the truncation level T = 25 on MLP backbone nets with tanh non-linearities. ... We examine four settings of the VAE structure sample size S = {1, 2, 4, 8}, along with latent variable sample sizes (M, K) = {(8, 8), (4, 16)} as in [18]. The truncation level is T = 25 with a maximum width O = 200. The distribution over the output from the decoding networks is factorized Bernoulli. ... All methods share the same maximum width of O = 200 and a latent variable dimensionality of 50. |