172 citations · 551 across the 12 of their papers we have counts for
3 papers · 1 filter
SyMetric: Measuring the Quality of Learnt Hamiltonian Dynamics Inferred from Vision
Irina Higgins, Peter Wirnsberger, Andrew Jaegle +1
A recently proposed class of models attempts to learn latent dynamics from high-dimensional observations, like images, using priors informed by Hamiltonian mechanics. While these m…
Which priors matter? Benchmarking models for learning latent dynamics
Aleksandar Botev, Andrew Jaegle, Peter Wirnsberger +2
Learning dynamics is at the heart of many important applications of machine learning (ML), such as robotics and autonomous driving. In these settings, ML algorithms typically need…
Normalizing flows for atomic solids
Peter Wirnsberger, George Papamakarios, Borja Ibarz +4
We present a machine-learning approach, based on normalizing flows, for modelling atomic solids. Our model transforms an analytically tractable base distribution into the target so…