1.3k citations · 6.3k across the 48 of their papers we have counts for
10 papers · 1 filter
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…
Reasoning-Modulated Representations
Petar Veličković, Matko Bošnjak, Thomas Kipf +4
Neural networks leverage robust internal representations in order to generalise. Learning them is difficult, and often requires a large training set that covers the data distributi…
CoBERL: Contrastive BERT for Reinforcement Learning
Andrea Banino, Adrià Puidomenech Badia, Jacob Walker +3
Many reinforcement learning (RL) agents require a large amount of experience to solve tasks. We propose Contrastive BERT for RL (CoBERL), an agent that combines a new contrastive l…
PonderNet: Learning to Ponder
Andrea Banino, Jan Balaguer, Charles Blundell
In standard neural networks the amount of computation used grows with the size of the inputs, but not with the complexity of the problem being learnt. To overcome this limitation w…
Emphatic Algorithms for Deep Reinforcement Learning
Ray Jiang, Tom Zahavy, Zhongwen Xu +4
Off-policy learning allows us to learn about possible policies of behavior from experience generated by a different behavior policy. Temporal difference (TD) learning algorithms ca…
Neural Algorithmic Reasoning
Petar Veličković, Charles Blundell
Algorithms have been fundamental to recent global technological advances and, in particular, they have been the cornerstone of technical advances in one field rapidly being applied…