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 [1].
Hypersolvers: Toward Fast Continuous-Depth Models
Authors: Michael Poli, Stefano Massaroli, Atsushi Yamashita, Hajime Asama, Jinkyoo Park
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental evaluations on standard benchmarks, such as sampling for continuous normalizing flows, reveal consistent pareto efficiency over classical numerical methods. |
| Researcher Affiliation | Academia | Michael Poli KAIST, Diff Eq ML EMAIL Stefano Massaroli The University of Tokyo, Diff Eq ML EMAIL Atsushi Yamashita The University of Tokyo EMAIL Hajime Asama The University of Tokyo EMAIL Jinkyoo Park KAIST EMAIL |
| Pseudocode | No | The paper provides mathematical formulations and equations but does not include structured pseudocode or an algorithm block. |
| Open Source Code | Yes | Supporting reproducibility code is at https://github.com/Diff Eq ML/diffeqml-research/tree/master/hypersolver |
| Open Datasets | Yes | We train standard convolutional Neural ODEs with input layer augmentation (Massaroli et al., 2020b) on MNIST and CIFAR10 datasets. |
| Dataset Splits | No | The paper mentions using 'training dataset' and 'test data' but does not explicitly specify a validation set or detailed split percentages (e.g., 80/10/10) needed for reproduction. |
| Hardware Specification | Yes | The measurements presented are collected on a single V100 GPU. |
| Software Dependencies | No | The paper mentions 'Torch Dyn (Poli et al., 2020) library' and 'Py Torch (Paszke et al., 2017) module implementation' but does not specify their version numbers. |
| Experiment Setup | Yes | Following this initial optimization step, 2 layer convolutional Euler hypersolvers, Hyper Euler, (4) are trained by residual fitting (6) on 10 epochs of the training dataset with solution mesh length set to K = 10. As ground truth labels, we utilize the solutions obtained via dopri5 with absolute and relative tolerances set to 10 4 on the same data. |