284 citations · 1.8k across the 21 of their papers we have counts for
Showing 2017 · stat.MLShow all
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stat.ML2017★ 127 cited
DARLA: Improving Zero-Shot Transfer in Reinforcement Learning
Irina Higgins, Arka Pal, Andrei A. Rusu +6
Domain adaptation is an important open problem in deep reinforcement learning (RL). In many scenarios of interest data is hard to obtain, so agents may learn a source policy in a s…
stat.ML2017★ 79 cited
SCAN: Learning Hierarchical Compositional Visual Concepts
Irina Higgins, Nicolas Sonnerat, Loic Matthey +7
The seemingly infinite diversity of the natural world arises from a relatively small set of coherent rules, such as the laws of physics or chemistry. We conjecture that these rules…