A Dynamical Central Limit Theorem for Shallow Neural Networks
Authors: Zhengdao Chen, Grant Rotskoff, Joan Bruna, Eric Vanden-Eijnden
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | 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.' |