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..
Beyond the Edge of Stability via Two-step Gradient Updates
Authors: Lei Chen, Joan Bruna
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical verification of all theorems are provided in Appendix B. We conduct an experiment on real data to show that our finding in the low-dimension setting in Theorem 1 is possible to generalize to high-dimensional setting. |
| Researcher Affiliation | Academia | 1Courant Institute of Mathematical Sciences, New York University, New York 2Center for Data Science, New York University, New York. |
| Pseudocode | No | The paper does not contain any explicitly labeled "Pseudocode" or "Algorithm" blocks. |
| Open Source Code | No | The paper does not include any statements about releasing code or provide links to a code repository. |
| Open Datasets | Yes | We run 3, 4, 5-layer Re LU MLPs on MNIST (Le Cun et al., 1998). |
| Dataset Splits | No | The paper does not explicitly provide details about training/validation/test dataset splits, only mentioning training on MNIST and a synthetic dataset. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models) used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | We run gradient descent with two learning rates η1 = 0.5, η2 = 2.6. ... We train such a model... with learning rate η = 2.2 = 1.1d... The learning rate is 1.02 Eo S threshold. ... with learning rate η = 1.05 and η = 1.25. ... with learning rates η = 0.5, 0.4, 0.35 and a small rate η = 0.1 (for 3-layer). |