1.3k citations · 6.3k across the 48 of their papers we have counts for
5 papers · 1 filter
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan, Alexander Pritzel, Charles Blundell
Deep neural networks (NNs) are powerful black box predictors that have recently achieved impressive performance on a wide spectrum of tasks. Quantifying predictive uncertainty in N…
Learning to reinforcement learn
Jane X Wang, Zeb Kurth-Nelson, Dhruva Tirumala +6
In recent years deep reinforcement learning (RL) systems have attained superhuman performance in a number of challenging task domains. However, a major limitation of such applicati…
Early Visual Concept Learning with Unsupervised Deep Learning
Irina Higgins, Loic Matthey, Xavier Glorot +5
Automated discovery of early visual concepts from raw image data is a major open challenge in AI research. Addressing this problem, we propose an unsupervised approach for learning…
Matching Networks for One Shot Learning
Oriol Vinyals, Charles Blundell, Timothy Lillicrap +2
Learning from a few examples remains a key challenge in machine learning. Despite recent advances in important domains such as vision and language, the standard supervised deep lea…
Deep Exploration via Bootstrapped DQN
Ian Osband, Charles Blundell, Alexander Pritzel +1
Efficient exploration in complex environments remains a major challenge for reinforcement learning. We propose bootstrapped DQN, a simple algorithm that explores in a computational…