4 papers · 1 filter
A Clifford Algebraic Approach to E(n)-Equivariant High-order Graph Neural Networks
Viet-Hoang Tran, Thieu N. Vo, Tho Tran Huu +1
Designing neural network architectures that can handle data symmetry is crucial. This is especially important for geometric graphs whose properties are equivariance under Euclidean…
Equivariant Polynomial Functional Networks
Thieu N. Vo, Viet-Hoang Tran, Tho Tran Huu +5
Neural Functional Networks (NFNs) have gained increasing interest due to their wide range of applications, including extracting information from implicit representations of data, e…
Equivariant Neural Functional Networks for Transformers
Viet-Hoang Tran, Thieu N. Vo, An Nguyen The +5
This paper systematically explores neural functional networks (NFN) for transformer architectures. NFN are specialized neural networks that treat the weights, gradients, or sparsit…
Tree-Sliced Wasserstein Distance: A Geometric Perspective
Viet-Hoang Tran, Trang Pham, Tho Tran +4
Many variants of Optimal Transport (OT) have been developed to address its heavy computation. Among them, notably, Sliced Wasserstein (SW) is widely used for application domains by…