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..
A Dynamical Central Limit Theorem for Shallow Neural Networks
Authors: Zhengdao Chen, Grant Rotskoff, Joan Bruna, Eric Vanden-Eijnden
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also complement these results with numerical experiments. |
| Researcher Affiliation | Academia | Zhengdao Chen: Department of Chemistry, Stanford University : Grant M. Rotskoff: Courant Institute of Mathematical Sciences, New York University : Joan Bruna: Center for Data Science, New York University : Eric Vanden-Eijnden: New York University |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | No | The paper describes a "student-teacher experiment" using data generated from a "teacher network" (synthetic data), but does not mention the use of any well-known public datasets or provide a link/citation for accessing the data used in their experiments. |
| Dataset Splits | No | The paper does not provide specific details about training, validation, or test dataset splits (e.g., percentages, sample counts, or a detailed splitting methodology). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | The setup for the numerical experiments is described in Appendix G.1, including details such as the learning rate and initialization: 'All experiments are run with a learning rate of 0.1, and the student neurons are initialized with parameters ci(0) = 1 and zi(0) from a Gaussian distribution with mean 0 and variance 1.' |