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LieTransformer: Equivariant self-attention for Lie Groups
Michael Hutchinson, Charline Le Lan, Sheheryar Zaidi +3
Group equivariant neural networks are used as building blocks of group invariant neural networks, which have been shown to improve generalisation performance and data efficiency th…
Equivariant Learning of Stochastic Fields: Gaussian Processes and Steerable Conditional Neural Processes
Peter Holderrieth, Michael Hutchinson, Yee Whye Teh
Motivated by objects such as electric fields or fluid streams, we study the problem of learning stochastic fields, i.e. stochastic processes whose samples are fields like those occ…
Behavior Priors for Efficient Reinforcement Learning
Dhruva Tirumala, Alexandre Galashov, Hyeonwoo Noh +8
As we deploy reinforcement learning agents to solve increasingly challenging problems, methods that allow us to inject prior knowledge about the structure of the world and effectiv…
Amortized Probabilistic Detection of Communities in Graphs
Yueqi Wang, Yoonho Lee, Pallab Basu +4
Learning community structures in graphs has broad applications across scientific domains. While graph neural networks (GNNs) have been successful in encoding graph structures, exis…
Importance Weighted Policy Learning and Adaptation
Alexandre Galashov, Jakub Sygnowski, Guillaume Desjardins +5
The ability to exploit prior experience to solve novel problems rapidly is a hallmark of biological learning systems and of great practical importance for artificial ones. In the m…
Bootstrapping Neural Processes
Juho Lee, Yoonho Lee, Jungtaek Kim +3
Unlike in the traditional statistical modeling for which a user typically hand-specify a prior, Neural Processes (NPs) implicitly define a broad class of stochastic processes with…