research
∙
06/06/2019
Towards Interpretable Reinforcement Learning Using Attention Augmented Agents
Inspired by recent work in attention models for image captioning and que...
share
research
∙
06/05/2018
Relational recurrent neural networks
Memory-based neural networks model temporal data by leveraging an abilit...
research
∙
06/04/2018
Relational inductive biases, deep learning, and graph networks
Artificial intelligence (AI) has undergone a renaissance recently, makin...
research
∙
02/13/2018
Learning to Search with MCTSnets
Planning problems are among the most important and well-studied problems...
research
∙
02/08/2018
Learning and Querying Fast Generative Models for Reinforcement Learning
A key challenge in model-based reinforcement learning (RL) is to synthes...
research
∙
07/19/2017
Imagination-Augmented Agents for Deep Reinforcement Learning
We introduce Imagination-Augmented Agents (I2As), a novel architecture f...
research
∙
07/19/2017
Learning model-based planning from scratch
Conventional wisdom holds that model-based planning is a powerful approa...
research
∙
04/07/2017
Recurrent Environment Simulators
Models that can simulate how environments change in response to actions ...
research
∙
03/06/2017
Neural Episodic Control
Deep reinforcement learning methods attain super-human performance in a ...
research
∙
01/30/2017
PathNet: Evolution Channels Gradient Descent in Super Neural Networks
For artificial general intelligence (AGI) it would be efficient if multi...
research
∙
11/22/2016
Variational Intrinsic Control
In this paper we introduce a new unsupervised reinforcement learning met...
research
∙
06/14/2016
Model-Free Episodic Control
State of the art deep reinforcement learning algorithms take many millio...
research
∙
06/13/2016
Matching Networks for One Shot Learning
Learning from a few examples remains a key challenge in machine learning...
research
∙
06/08/2016
Convolution by Evolution: Differentiable Pattern Producing Networks
In this work we introduce a differentiable version of the Compositional ...
research
∙
04/29/2016
Towards Conceptual Compression
We introduce a simple recurrent variational auto-encoder architecture th...
research
∙
03/16/2016
One-Shot Generalization in Deep Generative Models
Humans have an impressive ability to reason about new concepts and exper...
research
∙
09/09/2015
Continuous control with deep reinforcement learning
We adapt the ideas underlying the success of Deep Q-Learning to the cont...
research
∙
05/20/2015
Weight Uncertainty in Neural Networks
We introduce a new, efficient, principled and backpropagation-compatible...
research
∙
02/16/2015
DRAW: A Recurrent Neural Network For Image Generation
This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural ...
research
∙
01/16/2014
Stochastic Backpropagation and Approximate Inference in Deep Generative Models
We marry ideas from deep neural networks and approximate Bayesian infere...
research
∙
12/19/2013
Playing Atari with Deep Reinforcement Learning
We present the first deep learning model to successfully learn control p...
research
∙
09/26/2012
Efficient Natural Evolution Strategies
Efficient Natural Evolution Strategies (eNES) is a novel alternative to ...
research
∙
06/22/2011
Natural Evolution Strategies
This paper presents Natural Evolution Strategies (NES), a recent family ...
research
∙
12/15/2005
