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..
Provable Guarantees for Neural Networks via Gradient Feature Learning
Authors: Zhenmei Shi, Junyi Wei, Yingyu Liang
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our paper is purely theoretical in nature, and thus we do not anticipate an immediate negative ethical impact. |
| Researcher Affiliation | Academia | Zhenmei Shi , Junyi Wei , Yingyu Liang University of Wisconsin, Madison EMAIL,EMAIL,EMAIL |
| Pseudocode | Yes | Algorithm 1 Network Training via Gradient Descent |
| Open Source Code | No | The paper is theoretical and does not mention providing open-source code for its framework or any associated implementation. |
| Open Datasets | No | The paper analyzes theoretical data distributions (e.g., mixtures of Gaussians, parity functions) rather than using named publicly available datasets for empirical training. No specific access information to a public dataset is provided. |
| Dataset Splits | No | The paper is theoretical and works with mathematical models of data distributions rather than empirical datasets, so there are no specified training, validation, or test splits in the experimental sense. |
| Hardware Specification | No | The paper is purely theoretical and does not conduct empirical experiments, so it does not mention any hardware specifications. |
| Software Dependencies | No | The paper is purely theoretical and does not conduct empirical experiments, so it does not list any software dependencies with version numbers. |
| Experiment Setup | No | The paper discusses "proper hyper-parameter values" within its theoretical framework (e.g., in Theorem 3.12), and provides conditions for these values, but it does not specify concrete numerical values for an experimental setup. |