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..
Fundamental Convergence Analysis of Sharpness-Aware Minimization
Authors: Pham Khanh, Hoang-Chau Luong, Boris Mordukhovich, Dat Tran
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments are conducted on classification tasks using deep learning models to confirm the practical aspects of our analysis. |
| Researcher Affiliation | Academia | Pham Duy Khanh Ho Chi Minh City University of Education EMAIL Hoang-Chau Luong VNU-HCM University of Science EMAIL Boris S. Mordukhovich Wayne State University EMAIL Dat Ba Tran Wayne State University EMAIL |
| Pseudocode | Yes | Algorithm 1 Inexact Gradient Descent (IGD) Methods; Algorithm 1a General framework for normalized variants of SAM; Algorithm 2 IGDr; Algorithm 2a [Andriushchenko and Flammarion, 2022] Unnormalized Sharpness-Aware Minimization (USAM); Algorithm 2b [Korpelevich, 1976] Extragradient Method |
| Open Source Code | No | The paper does not explicitly provide a link to its source code or a statement of code release within the main text. |
| Open Datasets | Yes | The algorithms are tested on two widely used image datasets: CIFAR-10 [Krizhevsky et al., 2009] and CIFAR-100 [Krizhevsky et al., 2009]. |
| Dataset Splits | Yes | We train well-known deep neural networks including Res Net18 [He et al., 2016], Res Net34 [He et al., 2016], and Wide Res Net28-10 [Zagoruyko and Komodakis, 2016] on this dataset by using 10% of the training set as a validation set. |
| Hardware Specification | Yes | All the experiments are conducted on a computer with NVIDIA RTX 3090 GPU. |
| Software Dependencies | No | The paper mentions "SGD Momentum" as a base optimizer but does not specify version numbers for any software, libraries, or frameworks used in the experiments. |
| Experiment Setup | Yes | All the models are trained by using SAM with SGD Momentum as the base optimizer for 200 epochs and a batch size of 128. ...we set the initial stepsize to 0.1, momentum to 0.9, the ℓ2-regularization parameter to 0.001, and the perturbation radius ρ to 0.05. |