2.7k citations · 5.7k across the 63 of their papers we have counts for
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CompILE: Compositional Imitation Learning and Execution
Thomas Kipf, Yujia Li, Hanjun Dai +5
We introduce Compositional Imitation Learning and Execution (CompILE): a framework for learning reusable, variable-length segments of hierarchically-structured behavior from demons…
Strength in Numbers: Trading-off Robustness and Computation via Adversarially-Trained Ensembles
Edward Grefenstette, Robert Stanforth, Brendan O'Donoghue +3
While deep learning has led to remarkable results on a number of challenging problems, researchers have discovered a vulnerability of neural networks in adversarial settings, where…
Learning to Understand Goal Specifications by Modelling Reward
Dzmitry Bahdanau, Felix Hill, Jan Leike +4
Recent work has shown that deep reinforcement-learning agents can learn to follow language-like instructions from infrequent environment rewards. However, this places on environmen…
Can Neural Networks Understand Logical Entailment?
Richard Evans, David Saxton, David Amos +2
We introduce a new dataset of logical entailments for the purpose of measuring models' ability to capture and exploit the structure of logical expressions against an entailment pre…