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..
On the Symmetries of Deep Learning Models and their Internal Representations
Authors: Charles Godfrey, Davis Brown, Tegan Emerson, Henry Kvinge
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our work suggests that the symmetries of a network are propagated into the symmetries in that network s representation of data, providing us with a better understanding of how architecture affects the learning and prediction process. Finally, we speculate that for Re LU networks, the intertwiner groups may provide a justification for the common practice of concentrating model interpretability exploration on the activation basis in hidden layers rather than arbitrary linear combinations thereof. |
| Researcher Affiliation | Collaboration | 1Pacific Northwest National Laboratory, 2Department of Mathematics, University of Washington, 3Department of Mathematics, Colorado State University, 4Department of Mathematical Sciences, University of Texas, El Paso |
| Pseudocode | No | The paper refers to an 'algorithm that compute Gσn' in Section 3.1, but no pseudocode block or clearly labeled algorithm section is present in the main paper or appendices. |
| Open Source Code | No | We are in the process of making code publicly available. |
| Open Datasets | Yes | We conduct experiments stitching networks at Re LU activation layers with the stitching layer restricted to elements of the group GRe LU showing in fig. 1 that one can stitch CNNs on CIFAR-10 [Kri09]... ...We compare three models trained on Image Net |
| Dataset Splits | No | The paper mentions using 'validation accuracy' and 'lowest validation loss' (Section 4 and Appendix D.1), implying a validation set was used. However, it only explicitly details a 50,000 training images and 10,000 test images split for CIFAR-10 (Appendix D.1) and does not provide specific details on how the validation set was created or its size. |
| Hardware Specification | Yes | All models were trained on NVIDIA A100 GPUs. |
| Software Dependencies | No | The paper states: 'All models were trained using PyTorch [Pas+19].' (Appendix D.1). However, it does not provide a specific version number for PyTorch or any other software dependencies, which is required for reproducibility. |
| Experiment Setup | Yes | We train our models with SGD (for Myrtle CNNs) and Adam (for Res Net20s), using the same hyperparameters as described in [Pag18] and [Kri09] respectively. We use a batch size of 128 and run each experiment for 50 epochs. |