activity
20122024
most citedDeep Learning with Sets and Point Clouds

22 citations · 41 across the 10 of their papers we have counts for

collaborators

10 papers

cs.LG2024

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…

cs.LG20235 cited

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…

cs.LG20233 cited

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…

cs.LG2023

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…

cs.AI20222 cited

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.…

stat.ML20165 cited

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…