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..
Equi-normalization of Neural Networks
Authors: Pierre Stock, Benjamin Graham, Rémi Gribonval, Hervé Jégou
ICLR 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We analyze our approach by carrying out experiments on the MNIST and CIFAR-10 datasets and on the more challenging Image Net dataset. |
| Researcher Affiliation | Collaboration | Pierre Stock1,2, Benjamin Graham1, R emi Gribonval2 and Herv e J egou1 1Facebook AI Research 2Univ Rennes, Inria, CNRS, IRISA |
| Pseudocode | Yes | Algorithm 1: Pseudo-code of Equi-normalization |
| Open Source Code | Yes | Our code is available at https://github.com/facebookresearch/enorm. |
| Open Datasets | Yes | We analyze our approach by carrying out experiments on the MNIST and CIFAR-10 datasets and on the more challenging Image Net dataset. |
| Dataset Splits | Yes | We split the train set into one training set (40,000 images) and one validation set (10,000 images). |
| Hardware Specification | No | The paper mentions 'We train on 8 GPUs' but does not specify the model or type of the GPUs (e.g., NVIDIA A100, Tesla V100), nor does it provide details about CPUs or memory. |
| Software Dependencies | Yes | Although the additional overhead of implicit ENorm is theoretically negligible, we observed an increase of the training time of a Res Net-18 by roughly 30% using Py Torch 4.0 (Paszke et al., 2017). |
| Experiment Setup | Yes | We select the learning rate in {0.001, 0.01, 0.1} and decay it linearly to zero. We use a batch size of 256 and SGD with no momentum and a weight decay of 0.001. |