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..
AdaVAE: Bayesian Structural Adaptation for Variational Autoencoders
Authors: Paribesh Regmi, Rui Li
NeurIPS 2023 | Venue PDF | 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 EMAIL |
| 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. |