Neural Fisher Discriminant Analysis: Optimal Neural Network Embeddings in Polynomial Time
Authors: Burak Bartan, Mert Pilanci
ICML 2022 | Conference PDF | Archive PDF | Plain Text | 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. |