2.1k citations · 9.2k across the 77 of their papers we have counts for
7 papers · 1 filter
LipNet: End-to-End Sentence-level Lipreading
Yannis M. Assael, Brendan Shillingford, Shimon Whiteson +1
Lipreading is the task of decoding text from the movement of a speaker's mouth. Traditional approaches separated the problem into two stages: designing or learning visual features,…
Learning to Learn without Gradient Descent by Gradient Descent
Yutian Chen, Matthew W. Hoffman, Sergio Gomez Colmenarejo +4
We learn recurrent neural network optimizers trained on simple synthetic functions by gradient descent. We show that these learned optimizers exhibit a remarkable degree of transfe…
Learning to Perform Physics Experiments via Deep Reinforcement Learning
Misha Denil, Pulkit Agrawal, Tejas D Kulkarni +3
When encountering novel objects, humans are able to infer a wide range of physical properties such as mass, friction and deformability by interacting with them in a goal driven way…
Sample Efficient Actor-Critic with Experience Replay
Ziyu Wang, Victor Bapst, Nicolas Heess +4
This paper presents an actor-critic deep reinforcement learning agent with experience replay that is stable, sample efficient, and performs remarkably well on challenging environme…
Learning to learn by gradient descent by gradient descent
Marcin Andrychowicz, Misha Denil, Sergio Gomez +5
The move from hand-designed features to learned features in machine learning has been wildly successful. In spite of this, optimization algorithms are still designed by hand. In th…
Learning to Communicate with Deep Multi-Agent Reinforcement Learning
Jakob N. Foerster, Yannis M. Assael, Nando de Freitas +1
We consider the problem of multiple agents sensing and acting in environments with the goal of maximising their shared utility. In these environments, agents must learn communicati…