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How memory architecture affects learning in a simple POMDP: the two-hypothesis testing problem
Mario Geiger, Christophe Eloy, Matthieu Wyart
Reinforcement learning is generally difficult for partially observable Markov decision processes (POMDPs), which occurs when the agent's observation is partial or noisy. To seek go…
SE(3)-equivariant prediction of molecular wavefunctions and electronic densities
Oliver T. Unke, Mihail Bogojeski, Michael Gastegger +3
Machine learning has enabled the prediction of quantum chemical properties with high accuracy and efficiency, allowing to bypass computationally costly ab initio calculations. Inst…
Relative stability toward diffeomorphisms indicates performance in deep nets
Leonardo Petrini, Alessandro Favero, Mario Geiger +1
Understanding why deep nets can classify data in large dimensions remains a challenge. It has been proposed that they do so by becoming stable to diffeomorphisms, yet existing empi…
E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials
Simon Batzner, Albert Musaelian, Lixin Sun +6
This work presents Neural Equivariant Interatomic Potentials (NequIP), an E(3)-equivariant neural network approach for learning interatomic potentials from ab-initio calculations f…