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].
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. |