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..
Implicit regularization in Heavy-ball momentum accelerated stochastic gradient descent
Authors: Avrajit Ghosh, He Lyu, Xitong Zhang, Rongrong Wang
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We explore the implicit regularization in (SGD+M) and (GD+M) through a series of experiments validating our theory.6 NUMERICAL EXPERIMENTS |
| Researcher Affiliation | Academia | Avrajit Ghosh He Lyu Xitong Zhang Rongrong Wang Department of Computational Mathematics, Science and Engineering (CMSE) Michigan State University |
| Pseudocode | No | The paper provides mathematical formulations for the algorithms but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information (specific link, explicit statement of release) for the source code of the methodology described. |
| Open Datasets | Yes | Res Net-18 is used to classify a uniformly sub-sampled MNIST dataset with 1000 training images.trained to classify images from the CIFAR-10 and CIFAR-100 datasets. |
| Dataset Splits | No | The paper mentions using MNIST, CIFAR-10, and CIFAR-100 datasets but does not provide specific details on training, validation, or test dataset splits (e.g., percentages, sample counts, or explicit references to standard splits). |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, processor types, or memory used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., "Python 3.8, PyTorch 1.9"). |
| Experiment Setup | Yes | All external regularization schemes except learning rate decay and batch normalization have been turned off.We fix the batch-size to 640 in all our experiments.combinations of (h, β) chosen such that the effective learning rate h (1 β) remains same.Table 1 lists specific "β /h" values. |