LieTransformer: Equivariant self-attention for Lie Groups

Understand

Group equivariant neural networks are used as building blocks of group invariant neural networks, which have been shown to improve generalisation performance and data efficiency through principled parameter sharing.

• Such works have mostly focused on group equivariant convolutions, building on the result that group equivariant linear maps are necessarily convolutions.
• In this work, we extend the scope of the literature to self-attention, that is emerging as a prominent building block of deep learning models.
• We propose the LieTransformer, an architecture composed of LieSelfAttention layers that are equivariant to arbitrary Lie groups and their discrete subgroups.