3 citations · 3 across the 6 of their papers we have counts for
4 papers · 1 filter
Rotational Equivariance in Machine Learning: A Comprehensive Tutorial
Peter Lippmann, Fred A. Hamprecht
Rotational symmetry is one of the most important structural principles in machine learning on 3D data. In applications ranging from physics and materials science to 3D computer vis…
Equivariance by Local Canonicalization: A Matter of Representation
Gerrit Gerhartz, Peter Lippmann, Fred A. Hamprecht
Equivariant neural networks offer strong inductive biases for learning from molecular and geometric data but often rely on specialized, computationally expensive tensor operations.…
Beyond Canonicalization: How Tensorial Messages Improve Equivariant Message Passing
Peter Lippmann, Gerrit Gerhartz, Roman Remme +1
In numerous applications of geometric deep learning, the studied systems exhibit spatial symmetries and it is desirable to enforce these. For the symmetry of global rotations and r…
Theory and Approximate Solvers for Branched Optimal Transport with Multiple Sources
Peter Lippmann, Enrique Fita Sanmartín, Fred A. Hamprecht
Branched Optimal Transport (BOT) is a generalization of optimal transport in which transportation costs along an edge are subadditive. This subadditivity models an increase in tran…