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..
Understanding Gradient Descent on the Edge of Stability in Deep Learning
Authors: Sanjeev Arora, Zhiyuan Li, Abhishek Panigrahi
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The above theoretical results have been corroborated by an experimental study. |
| Researcher Affiliation | Academia | 1 Princeton University |
| Pseudocode | Yes | Algorithm 1 Perturbed Normalized Gradient Descent; Algorithm 3 Perturbed Gradient Descent on L |
| Open Source Code | No | The paper does not contain an explicit statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | We perform our experiments on a VGG-16 model (Simonyan & Zisserman, 2014) trained on CIFAR-10 dataset (Krizhevsky et al.) with Normalized GD and GD with L. |
| Dataset Splits | No | The paper mentions using a sample of training data but does not specify train/validation/test splits, percentages, or counts for any dataset. |
| Hardware Specification | No | The paper mentions training on a "single GPU" but does not specify any particular GPU model, CPU, or other hardware details. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | The network had 784 hidden units, with Ge LU activation function (Hendrycks & Gimpel, 2016). We used the loss function L as the mean squared loss to ensure the existence of minimizers and thus the manifold. For ef๏ฌcient training on a single GPU, we consider a sample of 1000 randomly selected points from the training data. |