Summary of Stochastic Neural Network Symmetrisation in Markov Categories, by Rob Cornish
Stochastic Neural Network Symmetrisation in Markov Categories
by Rob Cornish
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Category Theory (math.CT)
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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 A neural network symmetrization procedure is proposed, enabling conversion of H-equivariant networks to G-equivariant ones through group homomorphisms. This is formulated using Markov categories, allowing consideration of stochastic outputs while abstracting away measure-theoretic details. A flexible and compositional framework for symmetrisation is obtained, relying on minimal assumptions about the group and neural network architecture. The approach recovers existing techniques for deterministic models and extends to stochastic models, demonstrating the utility of Markov categories in machine learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Symmetrising a neural network means changing how it works with certain groups, like symmetry or rotation. Imagine taking a picture and rotating it – you want the same features to be aligned correctly. This is similar for neural networks, which need to understand patterns that are not changed by certain symmetries. The paper proposes a way to do this using something called Markov categories, which helps simplify complex problems in machine learning. |
Keywords
* Artificial intelligence * Machine learning * Neural network
