Daan Wierstra | DeepAI

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∙ 06/06/2019

Towards Interpretable Reinforcement Learning Using Attention Augmented Agents

Inspired by recent work in attention models for image captioning and que...

0 Alex Mott, et al. ∙

∙ 06/05/2018

Relational recurrent neural networks

Memory-based neural networks model temporal data by leveraging an abilit...

2 Adam Santoro, et al. ∙

∙ 06/04/2018

Relational inductive biases, deep learning, and graph networks

Artificial intelligence (AI) has undergone a renaissance recently, makin...

0 Peter W. Battaglia, et al. ∙

∙ 02/13/2018

Learning to Search with MCTSnets

Planning problems are among the most important and well-studied problems...

0 Arthur Guez, et al. ∙

∙ 02/08/2018

Learning and Querying Fast Generative Models for Reinforcement Learning

A key challenge in model-based reinforcement learning (RL) is to synthes...

0 Lars Buesing, et al. ∙

∙ 07/19/2017

Imagination-Augmented Agents for Deep Reinforcement Learning

We introduce Imagination-Augmented Agents (I2As), a novel architecture f...

0 Theophane Weber, et al. ∙

∙ 07/19/2017

Learning model-based planning from scratch

Conventional wisdom holds that model-based planning is a powerful approa...

0 Razvan Pascanu, et al. ∙

∙ 04/07/2017

Recurrent Environment Simulators

Models that can simulate how environments change in response to actions ...

0 Silvia Chiappa, et al. ∙

∙ 03/06/2017

Neural Episodic Control

Deep reinforcement learning methods attain super-human performance in a ...

0 Alexander Pritzel, et al. ∙

∙ 01/30/2017

PathNet: Evolution Channels Gradient Descent in Super Neural Networks

For artificial general intelligence (AGI) it would be efficient if multi...

0 Chrisantha Fernando, et al. ∙

∙ 11/22/2016

Variational Intrinsic Control

In this paper we introduce a new unsupervised reinforcement learning met...

0 Karol Gregor, et al. ∙

∙ 06/14/2016

Model-Free Episodic Control

State of the art deep reinforcement learning algorithms take many millio...

0 Charles Blundell, et al. ∙

∙ 06/13/2016

Matching Networks for One Shot Learning

Learning from a few examples remains a key challenge in machine learning...

0 Oriol Vinyals, et al. ∙

∙ 06/08/2016

Convolution by Evolution: Differentiable Pattern Producing Networks

In this work we introduce a differentiable version of the Compositional ...

0 Chrisantha Fernando, et al. ∙

∙ 04/29/2016

Towards Conceptual Compression

We introduce a simple recurrent variational auto-encoder architecture th...

0 Karol Gregor, et al. ∙

∙ 03/16/2016

One-Shot Generalization in Deep Generative Models

Humans have an impressive ability to reason about new concepts and exper...

0 Danilo Jimenez Rezende, et al. ∙

∙ 09/09/2015

Continuous control with deep reinforcement learning

We adapt the ideas underlying the success of Deep Q-Learning to the cont...

0 Timothy P. Lillicrap, et al. ∙

∙ 05/20/2015

Weight Uncertainty in Neural Networks

We introduce a new, efficient, principled and backpropagation-compatible...

0 Charles Blundell, et al. ∙

∙ 02/16/2015

DRAW: A Recurrent Neural Network For Image Generation

This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural ...

0 Karol Gregor, et al. ∙

∙ 01/16/2014

Stochastic Backpropagation and Approximate Inference in Deep Generative Models

We marry ideas from deep neural networks and approximate Bayesian infere...

0 Danilo Jimenez Rezende, et al. ∙

∙ 12/19/2013

Playing Atari with Deep Reinforcement Learning

We present the first deep learning model to successfully learn control p...

0 Volodymyr Mnih, et al. ∙

∙ 09/26/2012

Efficient Natural Evolution Strategies

Efficient Natural Evolution Strategies (eNES) is a novel alternative to ...

0 Yi Sun, et al. ∙

∙ 06/22/2011

Natural Evolution Strategies

This paper presents Natural Evolution Strategies (NES), a recent family ...

0 Daan Wierstra, et al. ∙

∙ 12/15/2005

Evolino for recurrent support vector machines

Traditional Support Vector Machines (SVMs) need pre-wired finite time wi...

0 Juergen Schmidhuber, et al. ∙

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