380 citations · 705 across the 18 of their papers we have counts for
5 papers · 1 filter
Meta-learning in natural and artificial intelligence
Jane X. Wang
Meta-learning, or learning to learn, has gained renewed interest in recent years within the artificial intelligence community. However, meta-learning is incredibly prevalent within…
Temporal Difference Uncertainties as a Signal for Exploration
Sebastian Flennerhag, Jane X. Wang, Pablo Sprechmann +7
An effective approach to exploration in reinforcement learning is to rely on an agent's uncertainty over the optimal policy, which can yield near-optimal exploration strategies in…
Amortized learning of neural causal representations
Nan Rosemary Ke, Jane. X. Wang, Jovana Mitrovic +2
Causal models can compactly and efficiently encode the data-generating process under all interventions and hence may generalize better under changes in distribution. These models a…
Deep Reinforcement Learning and its Neuroscientific Implications
Matthew Botvinick, Jane X. Wang, Will Dabney +2
The emergence of powerful artificial intelligence is defining new research directions in neuroscience. To date, this research has focused largely on deep neural networks trained us…
Causally Correct Partial Models for Reinforcement Learning
Danilo J. Rezende, Ivo Danihelka, George Papamakarios +11
In reinforcement learning, we can learn a model of future observations and rewards, and use it to plan the agent's next actions. However, jointly modeling future observations can b…