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..
Globally Gated Deep Linear Networks
Authors: Qianyi Li, Haim Sompolinsky
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our theory accurately captures the behavior of ο¬nite width GGDLNs trained with gradient descent (GD) dynamics. |
| Researcher Affiliation | Academia | 1Biophysics Graduate Program, Harvard University 2Center for Brain Science, Harvard University 3Edmond and Lily Safra Center for Brain Sciences, Hebrew University |
| Pseudocode | No | The paper describes mathematical derivations and theoretical concepts but does not include any pseudocode or explicitly labeled algorithm blocks. |
| Open Source Code | Yes | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] In supplementary material |
| Open Datasets | Yes | In Fig.3 , we show parameter regimes where the bias can increase (Fig.3 (a-c)) or decrease (Fig.3 (d-f)) with Ο on MNIST dataset [19] (Appendix C.3 ). |
| Dataset Splits | Yes | Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] See Appendix C |
| Hardware Specification | Yes | Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [Yes] See Appendix C |
| Software Dependencies | Yes | Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] See Appendix C |
| Experiment Setup | Yes | Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] See Appendix C |