3 citations · 3 across the 9 of their papers we have counts for
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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…
Surrogate Functionals for Machine-Learned Orbital-Free Density Functional Theory
Roman Remme, Fred A. Hamprecht
We introduce surrogate functionals: machine-learned energy functionals for orbital-free density functional theory (OF-DFT) which are defined not by universal fidelity to a physical…
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.…
Low-dimensional embeddings of high-dimensional data
Cyril de Bodt, Alex Diaz-Papkovich, Michael Bleher +18
Large collections of high-dimensional data have become nearly ubiquitous across many academic fields and application domains, ranging from biology to the humanities. Since working…
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…