7 papers
RoVE: Rotary Value Embeddings Attention for Relative Position-dependent Value Pathways
Alejandro GarcÃa-Castellanos, Maurice Weiler, Erik J Bekkers
Rotary Position Embeddings (RoPE) make attention scores position-relative but leave the value pathway position-blind: the message sent by a value token is the same regardless of it…
Uncertainty-aware Machine Learning Interatomic Potentials via Learned Functional Perturbations
Olga Zaghen, Maksim Zhdanov, Dario Coscia +2
Machine Learning Interatomic Potentials (MLIPs) achieve near ab initio accuracy at a fraction of the cost of quantum-mechanical simulations, yet they remain prone to silent failure…
Flowing with Confidence
Friso de Kruiff, Dario Coscia, Max Welling +1
Generative models can produce nonsensical text, unrealistic images, and unstable materials faster than simulation or human review can absorb; without per-sample confidence, trust e…
Generalized Reduction to the Isotropy for Flexible Equivariant Neural Fields
Alejandro GarcÃa-Castellanos, Gijs Bellaard, Remco Duits +2
Many geometric learning problems require invariants on heterogeneous product spaces, i.e., products of distinct spaces carrying different group actions, where standard techniques d…
Graph Homomorphism Distortion: A Metric to Distinguish Them All and in the Latent Space Bind Them
Martin Carrasco, Olga Zaghen, Kavir Sumaraj +2
A large driver of the complexity of graph learning is the interplay between structure and features. When analyzing the expressivity of graph neural networks, however, existing appr…
Pullback Flow Matching on Data Manifolds
Friso de Kruiff, Erik Bekkers, Ozan Ãktem +2
We propose Pullback Flow Matching (PFM), a novel framework for generative modeling on data manifolds. Unlike existing methods that assume or learn restrictive closed-form manifold…