150 citations · 789 across the 25 of their papers we have counts for
5 papers · 1 filter
Generating Diverse Programs with Instruction Conditioned Reinforced Adversarial Learning
Aishwarya Agrawal, Mateusz Malinowski, Felix Hill +3
Advances in Deep Reinforcement Learning have led to agents that perform well across a variety of sensory-motor domains. In this work, we study the setting in which an agent must le…
Neural Arithmetic Logic Units
Andrew Trask, Felix Hill, Scott Reed +3
Neural networks can learn to represent and manipulate numerical information, but they seldom generalize well outside of the range of numerical values encountered during training. T…
Measuring abstract reasoning in neural networks
David G. T. Barrett, Felix Hill, Adam Santoro +2
Whether neural networks can learn abstract reasoning or whether they merely rely on superficial statistics is a topic of recent debate. Here, we propose a dataset and challenge des…
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…
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
Alex Wang, Amanpreet Singh, Julian Michael +3
For natural language understanding (NLU) technology to be maximally useful, both practically and as a scientific object of study, it must be general: it must be able to process lan…