This is an abstract from a research paper called “Plain English Papers” Mathematical theory reveals hidden structure in symmetry-based neural networks. If you like this kind of analysis, you should join AImodels.fyi or follow us twitter.
Overview
- Equivariant neural network is a neural network with built-in symmetry.
- They were motivated by group representation theory, a way of describing how symmetries are encoded in mathematical structures.
- The layers of an equivariant neural network can be decomposed into simple representations that are the building blocks of more complex symmetries.
- Nonlinear activation functions such as rectified linear units (ReLU) produce interesting nonlinear equivariant mappings between these simple representations.
- This observation suggests the existence of a filtering or hierarchical structure in equivariant neural networks, which may help explain how they work.
simple english explanation
Equivariant neural network is a special type of neural network designed to symmetry. This means they are able to recognize the same patterns even if they change…