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 Fisher Discriminant Analysis: Optimal Neural Network Embeddings in Polynomial Time
Authors: Burak Bartan, Mert Pilanci
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the method s performance on synthetic and real datasets. |
| Researcher Affiliation | Academia | 1Department of Electrical Engineering, Stanford University, CA, USA. |
| Pseudocode | Yes | Algorithm 1 Binary NFDA for Re LU activation |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | We have tested the performance of the binary NFDA method on the two spirals dataset (Chalup & Wiklendt, 2007) |
| Dataset Splits | No | The paper mentions using training samples for the MNIST dataset ('n = 60000 training samples of the MNIST dataset') but does not specify explicit training, validation, or test splits, nor does it mention cross-validation or predefined splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'Python' and 'CVXPY (Diamond & Boyd, 2016)' but does not specify version numbers for these software components. |
| Experiment Setup | Yes | For the NFDA method, we have used P = 100 unique Dj matrices. |