44 citations · 116 across the 9 of their papers we have counts for
9 papers
Lorentz-Equivariant Geometric Algebra Transformers for High-Energy Physics
Jonas Spinner, Victor Bresó, Pim de Haan +3
Extracting scientific understanding from particle-physics experiments requires solving diverse learning problems with high precision and good data efficiency. We propose the Lorent…
FoMo Rewards: Can we cast foundation models as reward functions?
Ekdeep Singh Lubana, Johann Brehmer, Pim de Haan +1
We explore the viability of casting foundation models as generic reward functions for reinforcement learning. To this end, we propose a simple pipeline that interfaces an off-the-s…
Euclidean, Projective, Conformal: Choosing a Geometric Algebra for Equivariant Transformers
Pim de Haan, Taco Cohen, Johann Brehmer
The Geometric Algebra Transformer (GATr) is a versatile architecture for geometric deep learning based on projective geometric algebra. We generalize this architecture into a bluep…
Geometric Algebra Transformer
Johann Brehmer, Pim de Haan, Sönke Behrends +1
Problems involving geometric data arise in physics, chemistry, robotics, computer vision, and many other fields. Such data can take numerous forms, for instance points, direction v…
EDGI: Equivariant Diffusion for Planning with Embodied Agents
Johann Brehmer, Joey Bose, Pim de Haan +1
Embodied agents operate in a structured world, often solving tasks with spatial, temporal, and permutation symmetries. Most algorithms for planning and model-based reinforcement le…
Deconfounding Imitation Learning with Variational Inference
Risto Vuorio, Pim de Haan, Johann Brehmer +3
Standard imitation learning can fail when the expert demonstrators have different sensory inputs than the imitating agent. This is because partial observability gives rise to hidde…
Learning Lattice Quantum Field Theories with Equivariant Continuous Flows
Mathis Gerdes, Pim de Haan, Corrado Rainone +2
We propose a novel machine learning method for sampling from the high-dimensional probability distributions of Lattice Field Theories, which is based on a single neural ODE layer a…
Scaling Up Machine Learning For Quantum Field Theory with Equivariant Continuous Flows
Pim de Haan, Corrado Rainone, Miranda C. N. Cheng +1
We propose a continuous normalizing flow for sampling from the high-dimensional probability distributions of Quantum Field Theories in Physics. In contrast to the deep architecture…
Gauge Equivariant Mesh CNNs: Anisotropic convolutions on geometric graphs
Pim de Haan, Maurice Weiler, Taco Cohen +1
A common approach to define convolutions on meshes is to interpret them as a graph and apply graph convolutional networks (GCNs). Such GCNs utilize isotropic kernels and are theref…