schaul.site44.com

Tom Schaul, PhD - Google DeepMind

Tom Schaul, Ph.D. I am a senior research scientist at Google DeepMind. Include (modular/hierarchical) reinforcement learning, (stochastic/black-box) optimization with minimal hyperparameter tuning, and (deep/recurrent) neural networks. My favorite application domain are games. I grew up in Luxembourg and studied computer science in Switzerland (with exchanges at Waterloo. Where I obtained an MSc from the EPFL. In 2005. I hold a PhD from TU Munich. At the Swiss AI Lab IDSIA. For 40 additional ones. 10/12/...

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Tom Schaul, PhD - Google DeepMind | schaul.site44.com Reviews

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Tom Schaul, Ph.D. I am a senior research scientist at Google DeepMind. Include (modular/hierarchical) reinforcement learning, (stochastic/black-box) optimization with minimal hyperparameter tuning, and (deep/recurrent) neural networks. My favorite application domain are games. I grew up in Luxembourg and studied computer science in Switzerland (with exchanges at Waterloo. Where I obtained an MSc from the EPFL. In 2005. I hold a PhD from TU Munich. At the Swiss AI Lab IDSIA. For 40 additional ones. 10/12/...

<META> KEYWORDS

1 research

2 publications

3 software

4 in london

5 my research interests

6 and columbia

7 selected papers

8 arxiv

9 bibtex

10 supplementary material

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research,publications,software,in london,my research interests,and columbia,selected papers,arxiv,bibtex,supplementary material,code,slides,news,accepted this year,and unreal *,transfer learning *,meta learning 45,and games 44,cover,at csml,at ieee cig

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Tom Schaul, PhD - Google DeepMind | schaul.site44.com Reviews

https://schaul.site44.com

Tom Schaul, Ph.D. I am a senior research scientist at Google DeepMind. Include (modular/hierarchical) reinforcement learning, (stochastic/black-box) optimization with minimal hyperparameter tuning, and (deep/recurrent) neural networks. My favorite application domain are games. I grew up in Luxembourg and studied computer science in Switzerland (with exchanges at Waterloo. Where I obtained an MSc from the EPFL. In 2005. I hold a PhD from TU Munich. At the Swiss AI Lab IDSIA. For 40 additional ones. 10/12/...

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schaul.site44.com

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Tom Schaul - Publications

http://schaul.site44.com/publications.html

The Predictron: End-To-End Learning and Planning. David Silver*, Hado van Hasselt*, Matteo Hessel*, Tom Schaul*, Arthur Guez*, Tim Harley, Gabriel Dulac-Arnold, David Reichert, Neil Rabinowitz, Andre Barreto, Thomas Degris. FeUdal Networks for Hierarchical Reinforcement Learning. Alexander Sasha Vezhnevets, Simon Osindero, Tom Schaul, Nicolas Heess, Max Jaderberg, David Silver, Koray Kavukcuoglu. Reinforcement Learning with Unsupervised Auxiliary Tasks. ICLR-2017, Toulon). [arXiv]. Proceedings of the IEE...

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Tom Schaul, PhD - Google DeepMind

http://schaul.site44.com/index.html

Tom Schaul, Ph.D. I am a senior research scientist at Google DeepMind. Include (modular/hierarchical) reinforcement learning, (stochastic/black-box) optimization with minimal hyperparameter tuning, and (deep/recurrent) neural networks. My favorite application domain are games. I grew up in Luxembourg and studied computer science in Switzerland (with exchanges at Waterloo. Where I obtained an MSc from the EPFL. In 2005. I hold a PhD from TU Munich. At the Swiss AI Lab IDSIA. For 40 additional ones. 10/12/...

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Tom Schaul - Curriculum vitae

http://schaul.site44.com/cv.html

PhD in machine learning (dr. rer. nat.). Studies in Continuous Black-box Optimization" [26]. Advisor: Jürgen Schmidhuber. Eacute;cole Polytechnique Fédérale de Lausanne. MSc in computer engineering (dipl. ing. inf.). Evolving a Compact Concept-based Sokoban Solver" [1]. Cognitive Science and Machine Learning Summer School. Advanced Scientific Programming in Python Winter School. Machine Learning Summer School. Lycée Classique de Diekirch. National Research Fund Luxembourg. Kurzweil Best Paper Prize.

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Tom Schaul - PyBrain

http://schaul.site44.com/software.html

In order to make my research as reproducible as possible, most of the algorithms and benchmarks I use are available open-source through the PyBrain. PyBrain is written in Python, and while my contributions focus on (recurrent) neural networks, reinforcement learning and black-box optimization, it also contains algorithms for supervised learning, unsupervised learning, evolution, visualization, robotic simulations and more. To get started, get it from github. And cite our paper [16]. Title = { PyBrain} ,.

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Keynotes | IEEE Computational Inteligence in Games 2016 IEEE Computational Inteligence in Games 2016

http://cig16.image.ece.ntua.gr/keynotes

Keynotes IEEE Computational Inteligence in Games 2016 IEEE Computational Inteligence in Games 2016. IEEE CIG 2016 Committees. Special Sessions, Tutorials and Panel proposals. IEEE CIG 2016 Proceedings. IEEE CIG 2016 accepted papers. Twitter (IEEE Games TC). Anton Nijholt: BCI for Games - Games for BCI. Tom Schaul: General Intelligence and Games. Innes McKendrick: An iterative approach to the evaluation, application and design of PCG techniques. Is a programmer at UK indie games studio Hello Games, curren...

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Participants - Barbados 2015

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Institute of Cognitive Sciences and Technologies. Peter Sunehag, Google DeepMind.

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Tom Schaul, PhD - Google DeepMind

Tom Schaul, Ph.D. I am a senior research scientist at Google DeepMind. Include (modular/hierarchical) reinforcement learning, (stochastic/black-box) optimization with minimal hyperparameter tuning, and (deep/recurrent) neural networks. My favorite application domain are games. I grew up in Luxembourg and studied computer science in Switzerland (with exchanges at Waterloo. Where I obtained an MSc from the EPFL. In 2005. I hold a PhD from TU Munich. At the Swiss AI Lab IDSIA. For 40 additional ones. 10/12/...

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