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..
Neural Networks with Small Weights and Depth-Separation Barriers
Authors: Gal Vardi, Ohad Shamir
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we focus on feedforward Re LU networks, and prove fundamental barriers to proving such results beyond depth 4, by reduction to open problems and natural-proof barriers in circuit complexity. Our paper is structured as follows: In Section 2 we provide notations and definitions, followed by our results in Section 3. We sketch our proof ideas in Section 4, with all proofs deferred to Appendix A. |
| Researcher Affiliation | Academia | Gal Vardi Weizmann Institute of Science EMAIL Ohad Shamir Weizmann Institute of Science EMAIL |
| Pseudocode | No | The paper describes theoretical proofs and does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | This is a theoretical paper presenting proofs; it does not mention providing open-source code for any methodology. |
| Open Datasets | No | This is a theoretical paper and does not mention using or providing access to any specific datasets for training or evaluation. |
| Dataset Splits | No | This is a theoretical paper and does not involve empirical validation with dataset splits. |
| Hardware Specification | No | This is a theoretical paper and does not mention any hardware specifications used for experiments. |
| Software Dependencies | No | This is a theoretical paper and does not mention any specific software dependencies or versions for experimental setup. |
| Experiment Setup | No | This is a theoretical paper that focuses on mathematical proofs and does not describe any experimental setup or hyperparameters. |