Summary of Equivariant Neural Tangent Kernels, by Philipp Misof et al.
Equivariant Neural Tangent Kernels
by Philipp Misof, Pan Kessel, Jan E. Gerken
First submitted to arxiv on: 10 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper derives neural tangent kernels (NTKs) for a broad class of equivariant architectures based on group convolutions, taking an important step towards understanding the training dynamics of equivariant models. It shows that data augmentation and group convolutional networks share the same expected prediction at all training times and off-manifold, demonstrating similar training dynamics. Numerical experiments confirm this similarity holds approximately for finite-width ensembles. The paper also implements equivariant NTKs for roto-translations in the plane and 3d rotations, showing they outperform non-equivariant counterparts as kernel predictors for histological image classification and quantum mechanical property prediction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study helps us understand how neural networks that respect certain symmetries (equivariant) train. It finds that these networks behave similarly to those that don’t have this symmetry when training with extra data or augmented images. The research also shows that these equivariant networks can be better at predicting outcomes in certain tasks, like classifying medical images or calculating properties of tiny particles. |
Keywords
» Artificial intelligence » Data augmentation » Image classification
