Steerable Partial Differential Operators for Equivariant Neural Networks

Understand

Recent work in equivariant deep learning bears strong similarities to physics.

• Fields over a base space are fundamental entities in both subjects, as are equivariant maps between these fields.
• In deep learning, however, these maps are usually defined by convolutions with a kernel, whereas they are partial differential operators (PDOs) in physics.
• Developing the theory of equivariant PDOs in the context of deep learning could bring these subjects even closer together and lead to a stronger flow of ideas.