Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
A General Framework for Robust G-Invariance in G-Equivariant Networks
Authors: Sophia Sanborn, Nina Miolane
NeurIPS 2023 | Venue PDF | LLM Run Details | Input Tokens: 26,042 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,203 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our extensive experiments demonstrate improved scores in classification accuracy in traditional benchmark datasets as well as improved adversarial robustness. We examine the performance of the G-TC over Max G-Pooling in G-Equivariant Networks defined on these groups and trained on G-Invariant classification tasks. |
| Researcher Affiliation | Academia | Sophia Sanborn EMAIL Nina Miolane EMAIL Department of Electrical and Computer Engineering UC Santa Barbara |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is publicly available at https://github.com/sophiaas/gtc-invariance. |
| Open Datasets | Yes | For the groups SO(2) and O(2) acting on R2, we use the MNIST dataset of handwritten characters [37], and for the groups SO(3) and O(3) acting on R3, we use the voxelized Model Net10 database of 3D objects [52]. |
| Dataset Splits | Yes | A random 20% of the training dataset is set aside for model validation and is used to tune hyperparameters. The remaining 80% is used for training. |
| Hardware Specification | No | The paper does not specify the hardware used for experiments. |
| Software Dependencies | No | The paper mentions building upon the ESCNN library, but does not provide specific version numbers for software dependencies. |
| Experiment Setup | Yes | Full training details including hyperparameters are provided in Appendix G. All models are trained with a cross-entropy loss, using the Adam optimizer, a learning rate of 0.00005, weight decay of 0.00001, betas of [0.9, 0.999], epsilon of 10-8, a reduce-on-plateau learning rate scheduler with a factor of 0.5, patience of 2 epochs, and a minimum learning rate of 0.0.0001. Each model is trained with four random seeds [0, 1, 2, 3], and results are averaged across seeds. |