1 citations · 2 across the 5 of their papers we have counts for
5 papers
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…
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…
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…
Graph Pooling via Ricci Flow
Amy Feng, Melanie Weber
Graph Machine Learning often involves the clustering of nodes based on similarity structure encoded in the graph's topology and the nodes' attributes. On homophilous graphs, the in…