no code implementations • 7 Oct 2024 • Viet-Hoang Tran, Thieu N. Vo, Tho Tran Huu, Tan Minh Nguyen
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.
no code implementations • 5 Oct 2024 • Thieu N. Vo, Viet-Hoang Tran, Tho Tran Huu, An Nguyen The, Thanh Tran, Minh-Khoi Nguyen-Nhat, Duy-Tung Pham, Tan Minh Nguyen
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.
no code implementations • 5 Oct 2024 • Viet-Hoang Tran, Thieu N. Vo, An Nguyen The, Tho Tran Huu, Minh-Khoi Nguyen-Nhat, Thanh Tran, Duy-Tung Pham, Tan Minh Nguyen
This paper systematically explores neural functional networks (NFN) for transformer architectures.
