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 [1].
Adding One Neuron Can Eliminate All Bad Local Minima
Authors: SHIYU LIANG, Ruoyu Sun, Jason D. Lee, R. Srikant
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Under mild assumptions, we prove that after adding one special neuron with a skip connection to the output, or one special neuron per layer, every local minimum is a global minimum. |
| Researcher Affiliation | Academia | Shiyu Liang Coordinated Science Laboratory Dept. of Electrical and Computer Engineering University of Illinois at Urbana-Champaign EMAIL Ruoyu Sun Coordinated Science Laboratory Department of ISE University of Illinois at Urbana-Champaign EMAIL Jason D. Lee Marshall School of Business University of Southern California EMAIL R. Srikant Coordinated Science Laboratory Dept. of Electrical and Computer Engineering University of Illinois at Urbana-Champaign EMAIL |
| Pseudocode | No | The paper does not contain any sections or figures explicitly labeled 'Pseudocode' or 'Algorithm'. |
| Open Source Code | No | No statement about releasing source code or a link to a code repository for the methodology described in this paper was found. |
| Open Datasets | No | The paper discusses using a 'dataset D' for binary classification tasks and makes an assumption about its realizability, but it does not specify any named public dataset or provide concrete access information (link, DOI, repository, or formal citation with authors/year) for any dataset used or mentioned. |
| Dataset Splits | No | This paper is theoretical and does not describe empirical experiments, thus no information on training, validation, or test dataset splits is provided. |
| Hardware Specification | No | This paper is theoretical and does not describe empirical experiments, therefore no specific hardware specifications are mentioned. |
| Software Dependencies | No | This paper is theoretical and does not describe empirical experiments, therefore no specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | This paper is theoretical and focuses on mathematical proofs, and thus does not include details on experimental setup or hyperparameters. |