Daan Wierstra

发表

Evolving Cell Structures for Sequence Learning

Julian Togelius, Jürgen Schmidhuber, Justin Bayer, 2009, ICANN 2009.

A System for Robotic Heart Surgery that Learns to Tie Knots Using Recurrent Neural Networks

Jürgen Schmidhuber, Alois Knoll, Faustino J. Gomez, 2006 .

Stochastic search using the natural gradient

Tom Schaul, Yi Sun, Jürgen Schmidhuber, 2009, ICML '09.

One-shot Learning with Memory-Augmented Neural Networks pdf

Daan Wierstra, Matthew Botvinick, Timothy P. Lillicrap, 2016, ArXiv.

Robust player imitation using multiobjective evolution

Julian Togelius, Jürgen Schmidhuber, Daan Wierstra, 2009, 2009 IEEE Congress on Evolutionary Computation.

Imagination-Augmented Agents for Deep Reinforcement Learning

Razvan Pascanu, Demis Hassabis, David Silver, 2017, NIPS.

Matching Networks for One Shot Learning

Oriol Vinyals, Koray Kavukcuoglu, Daan Wierstra, 2016, NIPS.

Fitness Expectation Maximization

Tom Schaul, Jürgen Schmidhuber, Jan Peters, 2008, PPSN.

Recurrent policy gradients

Jürgen Schmidhuber, Alexander Förster, Jan Peters, 2010, Log. J. IGPL.

DRAW: A Recurrent Neural Network For Image Generation pdf

Alex Graves, Daan Wierstra, Karol Gregor, 2015, ICML.

Training Recurrent Networks by Evolino

Jürgen Schmidhuber, Faustino J. Gomez, Daan Wierstra, 2007, Neural Computation.

Relational recurrent neural networks

Razvan Pascanu, Oriol Vinyals, Daan Wierstra, 2018, NeurIPS.

A study in direct policy search

Daan Wierstra, Daan Wierstra, 2010 .

Continuous control with deep reinforcement learning pdf

Yuval Tassa, David Silver, Daan Wierstra, 2015, ICLR.

Stochastic Backpropagation and Approximate Inference in Deep Generative Models

Daan Wierstra, Shakir Mohamed, Danilo Jimenez Rezende, 2014, ICML.

Modeling systems with internal state using evolino

Jürgen Schmidhuber, Faustino J. Gomez, Daan Wierstra, 2005, GECCO '05.

PathNet: Evolution Channels Gradient Descent in Super Neural Networks pdf

Chrisantha Fernando, Daan Wierstra, Charles Blundell, 2017, ArXiv.

Relational inductive biases, deep learning, and graph networks pdf

Razvan Pascanu, H. Francis Song, Jessica B. Hamrick, 2018, ArXiv.

Convolution by Evolution: Differentiable Pattern Producing Networks

David Pfau, Max Jaderberg, Chrisantha Fernando, 2016, GECCO.

Efficient natural evolution strategies

Tom Schaul, Yi Sun, Jürgen Schmidhuber, 2009, GECCO.

Recurrent Environment Simulators pdf

Daan Wierstra, Shakir Mohamed, Silvia Chiappa, 2017, ICLR.

Policy Gradient Critics

Jürgen Schmidhuber, Daan Wierstra, J. Schmidhuber, 2007, ECML.

Weight Uncertainty in Neural Networks pdf

Julien Cornebise, Koray Kavukcuoglu, Daan Wierstra, 2015, ArXiv.

Human-level control through deep reinforcement learning

Shane Legg, Alex Graves, Marc G. Bellemare, 2015, Nature.

Learning to Search with MCTSnets

Rémi Munos, Oriol Vinyals, Karen Simonyan, 2018, ICML.

Variational Intrinsic Control pdf

Daan Wierstra, Karol Gregor, Danilo Jimenez Rezende, 2016, ICLR.

Evolving Memory Cell Structures for Sequence Learning

Julian Togelius, Jürgen Schmidhuber, Justin Bayer, 2009, ICANN.

One-Shot Generalization in Deep Generative Models

Daan Wierstra, Karol Gregor, Ivo Danihelka, 2016, ICML.

Utile distinction hidden Markov models

Marco Wiering, Daan Wierstra, Daan Wierstra, 2004, ICML.

A System for Robotic Heart Surgery that Learns to Tie Knots Using Recurrent Neural Networks

Jürgen Schmidhuber, Alois Knoll, Faustino J. Gomez, 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

Towards Conceptual Compression

Daan Wierstra, Karol Gregor, Ivo Danihelka, 2016, NIPS.

Episodic Reinforcement Learning by Logistic Reward-Weighted Regression

Tom Schaul, Jürgen Schmidhuber, Jan Peters, 2008, ICANN.

Ontogenetic and Phylogenetic Reinforcement Learning

Julian Togelius, Tom Schaul, Jürgen Schmidhuber, 2009, Künstliche Intell..

Deterministic Policy Gradient Algorithms

Guy Lever, Martin A. Riedmiller, David Silver, 2014, ICML.

Evolino for recurrent support vector machines pdf

Jürgen Schmidhuber, Faustino J. Gomez, Daan Wierstra, 2005, ESANN.

Comparison of Maximum Likelihood and GAN-based training of Real NVPs pdf

Peter Dayan, Daan Wierstra, Benigno Uria, 2017, ArXiv.

Stochastic Back-propagation and Variational Inference in Deep Latent Gaussian Models pdf

Daan Wierstra, Shakir Mohamed, Danilo Jimenez Rezende, 2014, ArXiv.

Evolino: Hybrid Neuroevolution / Optimal Linear Search for Sequence Prediction

Jürgen Schmidhuber, Daan Wierstra, Faustino Gomez, 2005, IJCAI 2005.

Neural Episodic Control

Demis Hassabis, Oriol Vinyals, Daan Wierstra, 2017, ICML.

Curiosity-driven optimization

Tom Schaul, Yi Sun, Jürgen Schmidhuber, 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

Variational Learning for Recurrent Spiking Networks

Wulfram Gerstner, Daan Wierstra, Danilo Jimenez Rezende, 2011, NIPS.

Exponential natural evolution strategies

Tom Schaul, Yi Sun, Jürgen Schmidhuber, 2010, GECCO '10.

Evolino: Hybrid Neuroevolution/Optimal Linear Search for Sequence Learning

Jürgen Schmidhuber, Faustino J. Gomez, Daan Wierstra, 2005, IJCAI.

Natural Evolution Strategies

Tom Schaul, Jan Peters, Daan Wierstra, 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

Playing Atari with Deep Reinforcement Learning pdf

Alex Graves, Koray Kavukcuoglu, Martin A. Riedmiller, 2013, ArXiv.

Meta-Learning with Memory-Augmented Neural Networks

Daan Wierstra, Matthew Botvinick, Timothy P. Lillicrap, 2016, ICML.

Model-Free Episodic Control pdf

Joel Z. Leibo, Demis Hassabis, Yazhe Li, 2016, ArXiv.

Towards Interpretable Reinforcement Learning Using Attention Augmented Agents

Alex Mott, Daan Wierstra, Mike Chrzanowski, 2019, NeurIPS.

Deep AutoRegressive Networks pdf

Daan Wierstra, Charles Blundell, Karol Gregor, 2013, ICML.

Weight Uncertainty in Neural Network

Julien Cornebise, Koray Kavukcuoglu, Daan Wierstra, 2015, ICML.

Exploring parameter space in reinforcement learning

Tom Schaul, Yi Sun, Frank Sehnke, 2010, Paladyn J. Behav. Robotics.

Building Machines that Learn and Think for Themselves: Commentary on Lake et al., Behavioral and Brain Sciences, 2017

Tom Schaul, Nando de Freitas, Shane Legg, 2017, 1711.08378.

Learning and Querying Fast Generative Models for Reinforcement Learning pdf

Fabio Viola, Demis Hassabis, Daan Wierstra, 2018, ArXiv.

S3TA: A Soft, Spatial, Sequential, Top-Down Attention Model

Alex Mott, Daan Wierstra, Mike Chrzanowski, 2018 .

Learning model-based planning from scratch pdf

Razvan Pascanu, Yujia Li, Oriol Vinyals, 2017, ArXiv.

Neural scene representation and rendering

Koray Kavukcuoglu, Daan Wierstra, Karol Gregor, 2018, Science.

Solving Deep Memory POMDPs with Recurrent Policy Gradients

Jürgen Schmidhuber, Alexander Förster, Jan Peters, 2007, ICANN.