# Daan Wierstra

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- Publications
- Influence

Human-level control through deep reinforcement learning

- V. Mnih, K. Kavukcuoglu, +16 authors Demis Hassabis
- Medicine, Computer Science
- Nature
- 26 February 2015

The theory of reinforcement learning provides a normative account, deeply rooted in psychological and neuroscientific perspectives on animal behaviour, of how agents may optimize their control of an… Expand

Continuous control with deep reinforcement learning

- T. Lillicrap, J. Hunt, +5 authors Daan Wierstra
- Computer Science, Mathematics
- ICLR
- 9 September 2015

We adapt the ideas underlying the success of Deep Q-Learning to the continuous action domain. We present an actor-critic, model-free algorithm based on the deterministic policy gradient that can… Expand

Playing Atari with Deep Reinforcement Learning

- V. Mnih, K. Kavukcuoglu, +4 authors Martin A. Riedmiller
- Computer Science
- ArXiv
- 19 December 2013

We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. The model is a convolutional neural network,… Expand

Stochastic Backpropagation and Approximate Inference in Deep Generative Models

- Danilo Jimenez Rezende, S. Mohamed, Daan Wierstra
- Computer Science, Mathematics
- ICML
- 16 January 2014

We marry ideas from deep neural networks and approximate Bayesian inference to derive a generalised class of deep, directed generative models, endowed with a new algorithm for scalable inference and… Expand

Matching Networks for One Shot Learning

- Oriol Vinyals, Charles Blundell, T. Lillicrap, K. Kavukcuoglu, Daan Wierstra
- Computer Science, Mathematics
- NIPS
- 13 June 2016

Learning from a few examples remains a key challenge in machine learning. Despite recent advances in important domains such as vision and language, the standard supervised deep learning paradigm does… Expand

Deterministic Policy Gradient Algorithms

- D. Silver, G. Lever, N. Heess, T. Degris, Daan Wierstra, Martin A. Riedmiller
- Computer Science
- ICML
- 21 June 2014

In this paper we consider deterministic policy gradient algorithms for reinforcement learning with continuous actions. The deterministic policy gradient has a particularly appealing form: it is the… Expand

Weight Uncertainty in Neural Networks

- Charles Blundell, Julien Cornebise, K. Kavukcuoglu, Daan Wierstra
- Mathematics, Computer Science
- ArXiv
- 20 May 2015

We introduce a new, efficient, principled and backpropagation-compatible algorithm for learning a probability distribution on the weights of a neural network, called Bayes by Backprop. It regularises… Expand

DRAW: A Recurrent Neural Network For Image Generation

- K. Gregor, Ivo Danihelka, A. Graves, Danilo Jimenez Rezende, Daan Wierstra
- Computer Science
- ICML
- 16 February 2015

This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural network architecture for image generation. DRAW networks combine a novel spatial attention mechanism that mimics the foveation… Expand

Natural Evolution Strategies

- Daan Wierstra, T. Schaul, Jan Peters, J. Schmidhuber
- Computer Science, Mathematics
- IEEE Congress on Evolutionary Computation (IEEE…
- 1 June 2008

This paper presents natural evolution strategies (NES), a novel algorithm for performing real-valued dasiablack boxpsila function optimization: optimizing an unknown objective function where… Expand

Relational inductive biases, deep learning, and graph networks

- P. Battaglia, Jessica B. Hamrick, +24 authors Razvan Pascanu
- Computer Science, Mathematics
- ArXiv
- 4 June 2018

Artificial intelligence (AI) has undergone a renaissance recently, making major progress in key domains such as vision, language, control, and decision-making. This has been due, in part, to cheap… Expand