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..
Stochastic Gradient and Langevin Processes
Authors: Xiang Cheng, Dong Yin, Peter Bartlett, Michael Jordan
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply our theoretical findings to studying the convergence of Stochastic Gradient Descent (SGD) for non-convex problems and corroborate them with experiments using SGD to train deep neural networks on the CIFAR-10 dataset. |
| Researcher Affiliation | Academia | 1Department of Electrical Engineering and Computer Science, University of California, Berkeley. Correspondence to: Xiang Cheng <EMAIL>. |
| Pseudocode | No | The paper describes algorithms and mathematical processes in text and equations, but does not include formal pseudocode blocks or algorithms labeled as such. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the methodology described. |
| Open Datasets | Yes | In all experiments, we use two different neural network architectures on the CIFAR-10 dataset (Krizhevsky & Hinton, 2009) with the standard test-train split. |
| Dataset Splits | Yes | In all experiments, we use two different neural network architectures on the CIFAR-10 dataset (Krizhevsky & Hinton, 2009) with the standard test-train split. |
| Hardware Specification | No | The paper does not specify any particular hardware details such as GPU models, CPU types, or memory used for the experiments. It only mentions using 'deep neural networks'. |
| Software Dependencies | No | The paper does not specify any software names with version numbers that would be necessary to reproduce the experiments. |
| Experiment Setup | Yes | In all of our experiments, we run SGD algorithm 2000 epochs such that the algorithm converges sufficiently. ... We choose constant step size δ from {0.001, 0.002, 0.004, 0.008, 0.016, 0.032, 0.064, 0.128} and minibatch size b from {32, 64, 128, 256, 512}. ... we do not use batch normalization or dropout, and use constant step size. |