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..
Model Selection for Bayesian Autoencoders
Authors: Ba-Hien Tran, Simone Rossi, Dimitrios Milios, Pietro Michiardi, Edwin V. Bonilla, Maurizio Filippone
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our approach qualitatively and quantitatively using a vast experimental campaign on a number of unsupervised learning tasks and show that, in small-data regimes where priors matter, our approach provides state-of-the-art results, outperforming multiple competitive baselines. |
| Researcher Affiliation | Collaboration | Ba-Hien Tran EURECOM (France) Simone Rossi EURECOM (France) Dimitrios Milios EURECOM (France) Pietro Michiardi EURECOM (France) Edwin V. Bonilla CSIRO s Data61 The Australian National University The University of Sydney (Australia) Maurizio Filippone EURECOM (France) |
| Pseudocode | No | The paper describes algorithms and methods but does not include a structured pseudocode block or algorithm section. |
| Open Source Code | No | The paper does not contain an explicit statement about releasing code or a link to a source code repository for the described methodology. |
| Open Datasets | Yes | MNIST [29]: We use 100 examples of the 0 digits to tune the prior. The training set consists of examples of 1-9 digits, whereas the test set contains 10 000 instances of all digits. FREY-YALE [12]: We use 1 956 examples of FREY faces to optimize the prior. The training set and test set are comprised of YALE faces. CELEBA dataset [30]. |
| Dataset Splits | Yes | MNIST [29]: We use 100 examples of the 0 digits to tune the prior. The training set consists of examples of 1-9 digits, whereas the test set contains 10 000 instances of all digits. ... For our proposal, we use 1 000 examples that are randomly chosen from the original training set to learn the prior distribution. The test set consists of about 20 000 images. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9). |
| Experiment Setup | Yes | Unless otherwise stated, all models including ours share the same latent dimensionality (K = 50). ... For our proposal, we use 1 000 examples that are randomly chosen from the original training set to learn the prior distribution. ... The test set consists of about 20 000 images. |