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..
Biological Learning of Irreducible Representations of Commuting Transformations
Authors: Alexander Genkin, David Lipshutz, Siavash Golkar, Tiberiu Tesileanu, Dmitri Chklovskii
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Simulations here are intended to demonstrate the following properties of our algorithms. |
| Researcher Affiliation | Academia | Alexander Genkin* David Lipshutz Siavash Golkar Tiberiu Te sileanu Dmitri B. Chklovskii*, *Neuroscience Institute, NYU Langone Medical School Center for Computational Neuroscience, Flatiron Institute |
| Pseudocode | Yes | Algorithm 1: The SVD algorithm with deflation |
| Open Source Code | Yes | All code for these experiments is included in the Supplementary material. |
| Open Datasets | Yes | We used natural images from the Van Hateren database [8] and digits from the MNIST dataset [9]. |
| Dataset Splits | No | The paper describes data generation and input sizes for simulations, and refers to general 'training details' in the checklist, but does not specify explicit train/validation/test splits (e.g., percentages or counts) for the datasets used. |
| Hardware Specification | Yes | This experiment took 14 minutes total on a Mac Book Pro with 3.5 GHz Dual-Core Intel Core i7 processor. |
| Software Dependencies | No | The paper mentions 'multi-layer perceptron' and 'bi-linear approximation' but does not specify any software libraries or their version numbers. |
| Experiment Setup | Yes | Learning rates were manually selected to be 5 10 4 for both algorithms. |