2.1k citations · 9.2k across the 77 of their papers we have counts for
8 papers · 1 filter
Building Machines that Learn and Think for Themselves: Commentary on Lake et al., Behavioral and Brain Sciences, 2017
M. Botvinick, D. G. T. Barrett, P. Battaglia +16
We agree with Lake and colleagues on their list of key ingredients for building humanlike intelligence, including the idea that model-based reasoning is essential. However, we favo…
Cortical microcircuits as gated-recurrent neural networks
Rui Ponte Costa, Yannis M. Assael, Brendan Shillingford +2
Cortical circuits exhibit intricate recurrent architectures that are remarkably similar across different brain areas. Such stereotyped structure suggests the existence of common co…
Few-shot Autoregressive Density Estimation: Towards Learning to Learn Distributions
Scott Reed, Yutian Chen, Thomas Paine +5
Deep autoregressive models have shown state-of-the-art performance in density estimation for natural images on large-scale datasets such as ImageNet. However, such models require m…
Robust Imitation of Diverse Behaviors
Ziyu Wang, Josh Merel, Scott Reed +3
Deep generative models have recently shown great promise in imitation learning for motor control. Given enough data, even supervised approaches can do one-shot imitation learning;…
The Intentional Unintentional Agent: Learning to Solve Many Continuous Control Tasks Simultaneously
Serkan Cabi, Sergio Gómez Colmenarejo, Matthew W. Hoffman +3
This paper introduces the Intentional Unintentional (IU) agent. This agent endows the deep deterministic policy gradients (DDPG) agent for continuous control with the ability to so…
Programmable Agents
Misha Denil, Sergio Gómez Colmenarejo, Serkan Cabi +2
We build deep RL agents that execute declarative programs expressed in formal language. The agents learn to ground the terms in this language in their environment, and can generali…