4 papers
Achieving Approximate Symmetry Is Exponentially Easier than Exact Symmetry
Behrooz Tahmasebi, Melanie Weber
Enforcing exact symmetry in machine learning models often yields significant gains in scientific applications, serving as a powerful inductive bias. However, recent work suggests t…
Neural Algorithmic Reasoning for Approximate -Coloring with Recursive Warm Starts
Knut Vanderbush, Melanie Weber
Node coloring is the task of assigning colors to the nodes of a graph such that no two adjacent nodes have the same color, while using as few colors as possible. It is the most wid…
Bispectral OT: Dataset Comparison using Symmetry-Aware Optimal Transport
Annabel Ma, Kaiying Hou, David Alvarez-Melis +1
Optimal transport (OT) is a widely used technique in machine learning, graphics, and vision that aligns two distributions or datasets using their relative geometry. In symmetry-ric…
Lie Algebra Canonicalization: Equivariant Neural Operators under arbitrary Lie Groups
Zakhar Shumaylov, Peter Zaika, James Rowbottom +3
The quest for robust and generalizable machine learning models has driven recent interest in exploiting symmetries through equivariant neural networks. In the context of PDE solver…