5 citations · 9 across the 23 of their papers we have counts for
7 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…
Demystifying the Token Dynamics of Deep Selective State Space Models
Thieu N Vo, Tung D. Pham, Xin T. Tong +1
Selective state space models (SSM), such as Mamba, have gained prominence for their effectiveness in modeling sequential data. Despite their outstanding empirical performance, a co…
Monomial Matrix Group Equivariant Neural Functional Networks
Viet-Hoang Tran, Thieu N. Vo, Tho H. Tran +2
Neural functional networks (NFNs) have recently gained significant attention due to their diverse applications, ranging from predicting network generalization and network editing t…
Noether's normalization in skew polynomial rings
Elad Paran, Thieu N. Vo
We study Noether's normalization lemma for finitely generated algebras over a division algebra. In its classical form, the lemma states that if is a proper ideal of the ring $R…
E(3)-Equivariant Mesh Neural Networks
Thuan Trang, Nhat Khang Ngo, Daniel Levy +3
Triangular meshes are widely used to represent three-dimensional objects. As a result, many recent works have address the need for geometric deep learning on 3D mesh. However, we o…