4 papers
Conditional Clifford-Steerable CNNs for PDE Modeling
Bálint László Szarvas, Maksim Zhdanov
We introduce Conditional Clifford-Steerable CNNs (C-CSCNNs), a unified framework that incorporates equivariance to arbitrary pseudo-Euclidean groups and significantly improves the…
Follow the Mean: Reference-Guided Flow Matching
Pedro M. P. Curvo, Maksim Zhdanov, Floor Eijkelboom +1
Existing approaches to controllable generation typically rely on fine-tuning, auxiliary networks, or test-time search. We show that flow matching admits a different control interfa…
AdS-GNN -- a Conformally Equivariant Graph Neural Network
Maksim Zhdanov, Nabil Iqbal, Erik Bekkers +1
Conformal symmetries, i.e.\ coordinate transformations that preserve angles, play a key role in many fields, including physics, mathematics, computer vision and (geometric) machine…
Clifford-Steerable Convolutional Neural Networks
Maksim Zhdanov, David Ruhe, Maurice Weiler +3
We present Clifford-Steerable Convolutional Neural Networks (CS-CNNs), a novel class of -equivariant CNNs. CS-CNNs process multivector fields on pseudo-Euclidean…