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..
Imitating Deep Learning Dynamics via Locally Elastic Stochastic Differential Equations
Authors: Jiayao Zhang, Hua Wang, Weijie Su
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We corroborate our theoretical analysis with experiments on a synthesized dataset of geometric shapes and CIFAR-10. |
| Researcher Affiliation | Academia | Jiayao Zhang Hua Wang Weijie J. Su University of Pennsylvania EMAIL |
| Pseudocode | No | The paper does not contain a clearly labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | Yes | Code for reproducing our experiments is publicly available at github.com:zjiayao/le_sde.git. |
| Open Datasets | Yes | We perform experiments on a synthesized dataset called GEOMNIST containing K = 3 types of geometric shapes (RECTANGLE, ELLIPSOID, and TRIANGLE) and on CIFAR-10 ([28], denoted by CIFAR) with K [2, 3] classes. |
| Dataset Splits | No | The paper mentions 'validation loss' and 'validation accuracies' but does not specify the exact percentages or absolute counts for training, validation, or test splits. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models or memory specifications used for experiments. |
| Software Dependencies | No | The paper does not provide specific software dependency versions (e.g., library names with version numbers). |
| Experiment Setup | Yes | All models are trained for T = 10^5 iterations (for GEOMNIST) or T = 3 * 10^5 iterations (for CIFAR) with a learning rate of 0.005 and a batch size of 1 under the softmax cross-entropy loss. |