206 citations · 470 across the 23 of their papers we have counts for
6 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…
Towards Learning Universal Audio Representations
Luyu Wang, Pauline Luc, Yan Wu +8
The ability to learn universal audio representations that can solve diverse speech, music, and environment tasks can spur many applications that require general sound content under…
Imitation by Predicting Observations
Andrew Jaegle, Yury Sulsky, Arun Ahuja +3
Imitation learning enables agents to reuse and adapt the hard-won expertise of others, offering a solution to several key challenges in learning behavior. Although it is easy to ob…
Perceiver IO: A General Architecture for Structured Inputs & Outputs
Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac +12
A central goal of machine learning is the development of systems that can solve many problems in as many data domains as possible. Current architectures, however, cannot be applied…
Perceiver: General Perception with Iterative Attention
Andrew Jaegle, Felix Gimeno, Andrew Brock +3
Biological systems perceive the world by simultaneously processing high-dimensional inputs from modalities as diverse as vision, audition, touch, proprioception, etc. The perceptio…