Model Selection for Bayesian Autoencoders
Authors: Ba-Hien Tran, Simone Rossi, Dimitrios Milios, Pietro Michiardi, Edwin V. Bonilla, Maurizio Filippone
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | 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. |