On the Power of Over-parametrization in Neural Networks with Quadratic Activation
Authors: Simon Du, Jason Lee
ICML 2018 | Conference PDF | Archive PDF | Plain Text | 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 <ssdu@cs.cmu.edu>. |
| 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. |