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..
Interpolation Technique to Speed Up Gradients Propagation in Neural ODEs
Authors: Talgat Daulbaev, Alexandr Katrutsa, Larisa Markeeva, Julia Gusak, Andrzej Cichocki, Ivan Oseledets
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | As a result, our method allows faster model training than the reverse dynamic method that was con๏ฌrmed and validated by extensive numerical experiments for several standard benchmarks. |
| Researcher Affiliation | Academia | Talgat Daulbaev, Alexandr Katrutsa, Larisa Markeeva, Julia Gusak, Andrzej Cichocki, Ivan Oseledets Skolkovo Institute of Science and Technology Moscow, Russia |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source code of the proposed method can be found at Git Hub1. 1https://github.com/Daulbaev/IRDM |
| Open Datasets | Yes | We tested these methods on four toy datasets (2spirals, pinwheel, moons and circles) and tabular miniboone dataset [23]. |
| Dataset Splits | No | No explicit description of training/validation/test dataset splits, or specific information about a dedicated validation set, was found. While 'test loss' is mentioned in the context of stopping criteria, it's not explicitly defined as a validation set. |
| Hardware Specification | Yes | Every separate experiment is conducted on a single NVIDIA Tesla V100 GPU with 16Gb of memory [21]. |
| Software Dependencies | No | The paper mentions 'torchdiffeq' and 'DOPRI5' but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | For toy datasets, we used the following hyperparameters: learning rate equals 10 3, the number of epochs was 10000, the batch size was 100, absolute and relative tolerances in the DOPRI5 solver were 10 5 and 10 5. |