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..
How Much Over-parameterization Is Sufficient to Learn Deep ReLU Networks?
Authors: Zixiang Chen, Yuan Cao, Difan Zou, Quanquan Gu
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we conduct some simple experiments to validate our theory. Since our paper mainly focuses on binary classification, we use a subset of the original CIFAR10 dataset (Krizhevsky et al., 2009), which only has two classes of images. |
| Researcher Affiliation | Academia | Department of Computer Science, University of California, Los Angles EMAIL |
| Pseudocode | Yes | Algorithm 1 Gradient descent with random initialization |
| Open Source Code | No | The paper does not contain any explicit statement about providing open-source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | we use a subset of the original CIFAR10 dataset (Krizhevsky et al., 2009) |
| Dataset Splits | No | The paper mentions using a subset of CIFAR10 for training and evaluating training error but does not specify any explicit training/validation/test dataset splits (e.g., percentages, counts, or predefined splits). |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library names with versions) needed to replicate the experiment. |
| Experiment Setup | No | The paper mentions training a '5-layer fully-connected Re LU network' and varying 'sample sizes' but does not provide specific hyperparameters such as learning rate, batch size, optimizer, or number of epochs for the experimental setup. |