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A Clifford Algebraic Approach to E(n)-Equivariant High-order Graph Neural Networks
In this paper, we introduce the Clifford Group Equivariant Graph Neural Networks (CG-EGNNs), a novel EGNN that enhances high-order message passing by integrating high-order local structures in the context of Clifford algebras.
Equivariant Polynomial Functional Networks
On the other hand, parameter-sharing-based NFNs built upon equivariant linear layers exhibit lower memory consumption and faster running time, yet their expressivity is limited due to the large size of the symmetric group of the input neural networks.
Equivariant Neural Functional Networks for Transformers
This paper systematically explores neural functional networks (NFN) for transformer architectures.
Tree-Sliced Wasserstein Distance: A Geometric Perspective
This structure is metrizable by a tree metric, which yields a closed-form expression for OT problems on tree systems.
