Imitating Deep Learning Dynamics via Locally Elastic Stochastic Differential Equations
Authors: Jiayao Zhang, Hua Wang, Weijie Su
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | 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 {zjiayao,wanghua,suw}@wharton.upenn.edu |
| 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. |