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 Dynamical System Perspective for Lipschitz Neural Networks
Authors: Laurent Meunier, Blaise J Delattre, Alexandre Araujo, Alexandre Allauzen
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | A comprehensive set of experiments on several datasets demonstrates the scalability of our architecture and the benefits as an ℓ2-provable defense against adversarial examples. |
| Researcher Affiliation | Collaboration | 1Miles Team, LAMSADE, Université Paris-Dauphine, PSL University, Paris, France 2Meta AI Research, Paris, France 3Foxstream, Lyon, France 4INRIA, Ecole Normale Supérieure, CNRS, PSL University, Paris, France 5ESPCI, Paris, France. |
| Pseudocode | Yes | Algorithm 1 Computation of a Convex Potential Layer |
| Open Source Code | Yes | Our code is available at https://github.com/ MILES-PSL/Convex-Potential-Layer |
| Open Datasets | Yes | We demonstrate the effectiveness of our approach on a classification task with CIFAR10 and CIFAR100 datasets (Krizhevsky et al., 2009). |
| Dataset Splits | No | The paper states the use of CIFAR10 and CIFAR100 datasets for experiments, which have standard training and test splits, but it does not explicitly describe the creation or size of a validation split (e.g., percentages or sample counts) used for model selection or hyperparameter tuning. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments, such as GPU models, CPU types, or cloud computing specifications. |
| Software Dependencies | No | The paper mentions leveraging 'Py Torch framework (Paszke et al., 2019)' but does not specify its version number or any other software dependencies with their versions. |
| Experiment Setup | Yes | We trained our networks with a batch size of 256 over 200 epochs. We use standard data augmentation (i.e., random cropping and flipping), a learning rate of 0.001 with Adam optimizer (Diederik P. Kingma, 2014) without weight decay and a piecewise triangular learning rate scheduler. We used a margin loss5 with margin parameter set to 0.7. |