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..
Learning Differential Equations that are Easy to Solve
Authors: Jacob Kelly, Jesse Bettencourt, Matthew J. Johnson, David K. Duvenaud
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate our approach by training substantially faster, while nearly as accurate, models in supervised classification, density estimation, and time-series modelling tasks. |
| Researcher Affiliation | Collaboration | Jacob Kelly University of Toronto, Vector Institute EMAIL Bettencourt University of Toronto, Vector Institute EMAIL James Johnson Google Brain EMAIL Duvenaud University of Toronto, Vector Institute EMAIL |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code available at: github.com/jacobjinkelly/easy-neural-ode |
| Open Datasets | Yes | We construct a model for MNIST classification: it takes in as input a flattened MNIST image... Physio Net Challenge 2012 dataset (Silva et al., 2012)... MINIBOONE tabular dataset from Papamakarios et al. (2017) and the MNIST image dataset (Le Cun et al., 2010). |
| Dataset Splits | No | The paper mentions 'training error' and 'test set' but does not explicitly describe training/validation/test splits, percentages, or specific sample counts for reproduction. |
| Hardware Specification | No | The paper does not specify any particular hardware (e.g., GPU or CPU models, memory details) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'JAX Python library' and 'standard dopri5 Runge-Kutta 4(5) solver' but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | During training, we weigh this regularization term by a hyperparameter λ and add it to our original loss to get our regularized objective... The default tolerance of 1.4e-8 for both atol and rtol behaved well in all our experiments. |