2 citations · 2 across the 10 of their papers we have counts for
7 papers · 1 filter
MuonSSM: Orthogonalizing State Space Models for Sequence Modeling
Thai-Khanh Nguyen, Ngoc-Bich-Uyen Vo, Thieu N. Vo +2
State space models (SSMs) have emerged as efficient linear-time alternatives to attention for long-sequence modeling. However, existing SSMs often suffer from instability and memor…
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…