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..
LyaNet: A Lyapunov Framework for Training Neural ODEs
Authors: Ivan Dario Jimenez Rodriguez, Aaron Ames, Yisong Yue
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Relative to standard Neural ODE training, we empirically ο¬nd that Lya Net can offer improved prediction performance, faster convergence of inference dynamics, and improved adversarial robustness. |
| Researcher Affiliation | Collaboration | 1Department of Computational and Mathematical Sciences, California Institute of Technology 2Argo AI. |
| Pseudocode | Yes | Algorithm 1 Monte Carlo Lya Net Training Algorithm 2 Path Integral Lya Net Training |
| Open Source Code | Yes | Our code is available at https://github. com/ivandariojr/Lyapunov Learning. |
| Open Datasets | Yes | We evaluate primarily on three computer vision datasets: Fashion MNIST, CIFAR-10 and CIFAR-100. |
| Dataset Splits | Yes | We found this by performing a grid search on learning rates and batch sizes over (0.1, 0.001, 0.001) (32, 64, 128), validated on a held out set of 10% of training data. |
| Hardware Specification | Yes | Our experiments ran on a cluster 6 GPUs: 4 Ge Force 1080 GPUs, 1 Titan X and Titan RTX. All experiments were able to run on less than 10GB of VRAM. |
| Software Dependencies | No | The paper mentions 'Nero (Liu et al., 2021)' and 'PGD as implemented by Kim (2020)' but does not specify version numbers for these software components or libraries. |
| Experiment Setup | Yes | To simplify tuning, we trained our models using Nero (Liu et al., 2021) with a learning rate of 0.01 with a batch size of 64 for models trained with Lya Net and 128 for models trained with regular backpropagation. We found this by performing a grid search on learning rates and batch sizes over (0.1, 0.001, 0.001) (32, 64, 128), validated on a held out set of 10% of training data. All models were trained for a total of 120 epochs. For our adversarial attack we used PGD as implemented by Kim (2020) for 10 iterations with a step size Ξ± = 2 255. |