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Learning How to Break Symmetry With Symmetry-Preserving Neural Networks - IPAM at UCLA
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- 1 Intro
- 2 Overview
- 3 Why is symmetry useful
- 4 Invariant vs equivariant models
- 5 Equivariant methods
- 6 Global symmetry equivalent
- 7 Questions
- 8 Applications
- 9 Molecular force fields
- 10 Scaling
- 11 Longrange interactions
- 12 Predicting charge density
- 13 Using a neural network
- 14 First paper
- 15 Competition
- 16 Efficiency
- 17 Advanced properties
- 18 Neural networks
- 19 Representation theory
- 20 Reducible representation
- 21 Future spaces
- 22 Spherical harmonic projections
- 23 Invariance
- 24 General questions
- 25 Emergent behavior
- 26 Curious principle
- 27 Structural phase transitions
- 28 First case
- 29 Order parameters
- 30 Why not just train a model
- 31 Another example
