22 citations · 41 across the 10 of their papers we have counts for
10 papers
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…
Lie Point Symmetry and Physics Informed Networks
Tara Akhound-Sadegh, Laurence Perreault-Levasseur, Johannes Brandstetter +2
Symmetries have been leveraged to improve the generalization of neural networks through different mechanisms from data augmentation to equivariant architectures. However, despite t…
Equivariant Adaptation of Large Pretrained Models
Arnab Kumar Mondal, Siba Smarak Panigrahi, Sékou-Oumar Kaba +2
Equivariant networks are specifically designed to ensure consistent behavior with respect to a set of input transformations, leading to higher sample efficiency and more accurate a…
Using Multiple Vector Channels Improves E(n)-Equivariant Graph Neural Networks
Daniel Levy, Sékou-Oumar Kaba, Carmelo Gonzales +2
We present a natural extension to E(n)-equivariant graph neural networks that uses multiple equivariant vectors per node. We formulate the extension and show that it improves perfo…
Utility Theory for Sequential Decision Making
Mehran Shakerinava, Siamak Ravanbakhsh
The von Neumann-Morgenstern (VNM) utility theorem shows that under certain axioms of rationality, decision-making is reduced to maximizing the expectation of some utility function.…
Annealing Gaussian into ReLU: a New Sampling Strategy for Leaky-ReLU RBM
Chun-Liang Li, Siamak Ravanbakhsh, Barnabas Poczos
Restricted Boltzmann Machine (RBM) is a bipartite graphical model that is used as the building block in energy-based deep generative models. Due to numerical stability and quantifi…