arXiv:2608.31045v1 Announce Type: new
Abstract: Rotational symmetry is one of the most important structural principles in machine learning on 3D data. In applications ranging from physics and materia...
By Peter Lippmann, Fred A. Hamprecht
arXiv:2606. 29584v1 Announce Type: cross Abstract: $\mathrm{Cl}(3,0)$ interatomic potentials, despite their algebraic elegance, predict force magnitudes accurately but force directions poorly.
By Can Polat, Erchin Serpedin, Mustafa Kurban, Hasan Kurban
arXiv:2608.28853v1 Announce Type: cross
Abstract: Equivariant graph neural networks provide a principled way to model geometric systems, but efficient first-order architectures remain limited in how...
By Alessio Borgi, Mario Severino, Fabrizio Silvestri, Pietro Li\`o
arXiv:2609.15083v1 Announce Type: new
Abstract: Mixed-curvature representation learning seeks to capture rich geometric structures that cannot be adequately modeled by a single curvature regime. Exis...
By Xingrun Li, Yusuke Mukuta, Xin Yang, Yinyu Ye, Tatsuya Harada
arXiv:2609.08381v1 Announce Type: cross
Abstract: Equivariant networks are commonly trained with Adam, yet recent work reports that matrix-structured optimizers such as Muon can perform better on the...
By Andrei Manolache, Mathias Niepert
arXiv:2610.01322v1 Announce Type: cross
Abstract: We introduce the Clifford Sheaf Neural Network (CSNN), an equivariant sheaf neural network for geometric graphs that places a Clifford algebra on eac...
By Kotaro Kamiya, Joel Nicholls