67 citations · 191 across the 16 of their papers we have counts for
Showing 2019 · cs.LGShow all
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cs.LG2019★ 22 cited
Analysing Deep Reinforcement Learning Agents Trained with Domain Randomisation
Tianhong Dai, Kai Arulkumaran, Tamara Gerbert +3
Deep reinforcement learning has the potential to train robots to perform complex tasks in the real world without requiring accurate models of the robot or its environment. A practi…
cs.LG2019★ 8 cited
Modular Meta-Learning with Shrinkage
Yutian Chen, Abram L. Friesen, Feryal Behbahani +4
Many real-world problems, including multi-speaker text-to-speech synthesis, can greatly benefit from the ability to meta-learn large models with only a few task-specific components…