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Peter Wirnsberger

2 accepted papers

2021

SyMetric: Measuring the Quality of Learnt Hamiltonian Dynamics Inferred from Vision

NeurIPS 2021poster

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 models have important potential applications in areas like robotics or autonomous driving, there is currently no good way…

2021

Which priors matter? Benchmarking models for learning latent dynamics

NeurIPS 2021poster

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 to reason about a physical system using high dimensional observations, such as images, without access to the underlying…

Cited by 33SourcecodeScholar