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..
A Symmetry-Aware Exploration of Bayesian Neural Network Posteriors
Authors: Olivier Laurent, Emanuel Aldea, Gianni Franchi
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This paper presents one of the first large-scale explorations of the posterior distribution of deep Bayesian Neural Networks (BNNs), expanding its study to real-world vision tasks and architectures. |
| Researcher Affiliation | Academia | Olivier Laurent,1,2 Emanuel Aldea1 & Gianni Franchi2, SATIE, Paris-Saclay University,1 U2IS, ENSTA Paris, Polytechnic Institute of Paris2 |
| Pseudocode | No | No structured pseudocode or algorithm blocks with explicit labels like "Algorithm" or "Pseudocode" were found in the paper. |
| Open Source Code | Yes | To help replicate our work, we share the source code of our experiments on Git Hub, notably including code to remove symmetries from neural networks a posteriori. |
| Open Datasets | Yes | To ensure transparency and accessibility, we use publicly available datasets, including MNIST, Fashion MNIST, CIFAR100, SVHN, Image Net-200, and Textures. Please refer to Appendix C.2.2 for details on these datasets. |
| Dataset Splits | No | No explicit statement providing specific percentages or sample counts for training, validation, and test splits across all datasets was found. |
| Hardware Specification | Yes | This work was performed using HPC resources from GENCI-IDRIS (Grant 2023[AD011011970R3])." and "training a substantial number of checkpoints for estimating the posterior, especially in the case of the thousand models trained on Tiny Image Net, was energy intensive (around 3 Nvidia V100 hours per training). |
| Software Dependencies | No | No explicit version numbers for software dependencies were provided in the text; for example, it mentions 'Torch Uncertainty' and 'cvxpy (Diamond & Boyd, 2016)' without specific versions. |
| Experiment Setup | Yes | We train Optu Net for 60 epochs with batches of size 64 using stochastic gradient descent (SGD) with a start learning rate of 0.04 and a weight decay of 2 10 4. We decay the learning rate twice during training, at epochs 15 and 30, dividing the learning rate by 2. |