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].
The Selective $G$-Bispectrum and its Inversion: Applications to $G$-Invariant Networks
Authors: Simon Mataigne, Johan Mathe, Sophia Sanborn, Christopher Hillar, Nina Miolane
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We prove desirable mathematical properties of the selective G-Bispectrum and demonstrate how its integration in neural networks enhances accuracy and robustness compared to traditional approaches, while enjoying considerable speeds-up compared to the full G-Bispectrum. |
| Researcher Affiliation | Collaboration | Simon Mataigne ICTEAM, UCLouvain Louvain-la-Neuve, Belgium EMAIL Johan Mathe Atmo San Francisco, CA EMAIL Sophia Sanborn Science San Francisco, CA EMAIL Christopher Hillar Algebraic San Francisco, CA EMAIL Nina Miolane UC Santa Barbara Santa Barbara, CA EMAIL |
| Pseudocode | Yes | Algorithm 1 Selective G-Bispectrum on any finite group G |
| Open Source Code | Yes | Our implementation of the selective G-bispectrum layer is based on the gtc-invariance repository, implementing the G-CNN with G-convolution and G-TC layer [28] and relying itself on the escnn library [3, 32]. The implementations related to this section can be found at the g-invariance repository. |
| Open Datasets | Yes | We run extensive experiments on the MNIST [23] and EMNIST [5] datasets to evaluate how each invariant layer (Max G-pooling, G-TC, selective G-Bispectrum) impacts accuracy and speed on classification tasks. |
| Dataset Splits | Yes | We obtain transformed versions of the datasets G-MNIST/EMNIST by applying a random action g G on each image in the original dataset. ... We assess the accuracy by averaging the validation accuracy over 10 runs. |
| Hardware Specification | Yes | The experiments a performed using 8 cores of a NVIDIA A30 GPU. |
| Software Dependencies | No | The paper mentions relying on the 'escnn library [3, 32]' and 'gtc-invariance repository' but does not specify version numbers for these software components or other dependencies. |
| Experiment Setup | Yes | The neural network architecture is composed of a G-convolution, a G-invariant layer, and finally a Multi-Layer-Perceptron (MLP), itself composed of three fully connected layers with Re LU nonlinearity. Finally, a fully connected linear layer is added to perform classification. The MLP s widths are tuned to match the number of parameters across each neural network model. The details are given in Appendix G. |