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..
On the Power of Over-parametrization in Neural Networks with Quadratic Activation
Authors: Simon Du, Jason Lee
ICML 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide new theoretical insights on why overparametrization is effective in learning neural networks. |
| Researcher Affiliation | Academia | 1Machine Learning Department, Carnegie Mellon University 2Department of Data Sciences and Operations, University of Southern California. Correspondence to: Simon S. Du <EMAIL>. |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any statements about releasing open-source code for the described methodology, nor does it include links to a code repository. |
| Open Datasets | No | The paper refers to 'n training data points' and discusses theoretical properties under assumptions like 'data is sampled from a regular distribution such as Gaussian.' It mentions 'arbitrary data set' and 'xi ~ N(0, I)' for theoretical analysis, but these are not actual, publicly accessible datasets used in empirical experiments. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments, thus no specific dataset splits (training, validation, test) are mentioned. |
| Hardware Specification | No | The paper is theoretical and does not describe empirical experiments, therefore no specific hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe empirical experiments, therefore no specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments, therefore no specific experimental setup details such as hyperparameters or training configurations are provided. |