25,161
edits
Changes
Created page with "'''Home * People * Volodymyr Mnih''' FILE:VolodymyrMnih.jpg|border|right|thumb|link=https://www.cs.toronto.edu/~vmnih/| Volodymyr Mnih <ref>[https://www...."
'''[[Main Page|Home]] * [[People]] * Volodymyr Mnih'''
[[FILE:VolodymyrMnih.jpg|border|right|thumb|link=https://www.cs.toronto.edu/~vmnih/| Volodymyr Mnih <ref>[https://www.cs.toronto.edu/~vmnih/ Vlad Mnih - Homepage]</ref> ]]
'''Volodymyr Mnih''',<br/>
a Canadian research scientist at at [[Google]] [[DeepMind]] with expertise in [[Deep Learning|deep learning]], heading the team working on [[Neural Networks#Deep|deep Q-networks]] (DQN) mastering [https://en.wikipedia.org/wiki/Atari_Games Atari games] <ref>[https://propecta.com/googles-ai-can-beat-atari-games We’ll Never Win! Google’s AI Plays Atari - Propecta] by [https://www.linkedin.com/in/natedame/en Nate Dame], March 19, 2015</ref>. DQNs were tested with games such as [https://en.wikipedia.org/wiki/Pong Pong], [https://en.wikipedia.org/wiki/Space_Invaders Space Invaders], [https://en.wikipedia.org/wiki/Breakout_(video_game) Breakout] and [https://en.wikipedia.org/wiki/Seaquest_(video_game) Seaquest], receiving only the pixels and the game score as inputs, to surpass the performance of all previous algorithms and achieve a level comparable to that of a professional human games tester across a set of 49 games, using the same algorithm, network architecture and hyperparameters. Volodymyr Mnih holds a Ph.D. in [[Learning|machine learning]] from [[University of Toronto]] under supervision of [[Mathematician#GEHinton|Geoffrey E. Hinton]], and a Master's degree in computing science fro [[University of Alberta]] where his advisor was [[Csaba Szepesvári]].
=Selected Publications=
<ref>[http://dblp.uni-trier.de/pers/hd/m/Mnih:Volodymyr dblp: Volodymyr Mnih]</ref>
==2008==
* [[Volodymyr Mnih]] ('''2008'''). ''Efficient Stopping Rules''. Masters thesis, [[University of Alberta]], advisor: [[Csaba Szepesvári]], [https://www.cs.toronto.edu/~vmnih/docs/msc-thesis.pdf pdf]
==2010 ...==
* [[Volodymyr Mnih]], [[Mathematician#GEHinton|Geoffrey E. Hinton]] ('''2010'''). ''[http://link.springer.com/chapter/10.1007/978-3-642-15567-3_16 Learning to Detect Roads in High-Resolution Aerial Images]''. [http://dblp.uni-trier.de/db/conf/eccv/eccv2010-6.html#MnihH10 ECCV 2010]
* [[Volodymyr Mnih]] ('''2013'''). ''Machine Learning for Aerial Image Labeling''. Ph.D. thesis, [[University of Toronto]], advisor [[Mathematician#GEHinton|Geoffrey E. Hinton]], [https://www.cs.toronto.edu/~vmnih/docs/Mnih_Volodymyr_PhD_Thesis.pdf pdf]
* [[Volodymyr Mnih]], [[Koray Kavukcuoglu]], [[David Silver]], [[Alex Graves]], [[Ioannis Antonoglou]], [[Daan Wierstra]], [[Martin Riedmiller]] ('''2013'''). ''Playing Atari with Deep Reinforcement Learning''. [http://arxiv.org/abs/1312.5602 arXiv:1312.5602] <ref>[http://www.nervanasys.com/demystifying-deep-reinforcement-learning/ Demystifying Deep Reinforcement Learning] by [http://www.nervanasys.com/author/tambet/ Tambet Matiisen], [http://www.nervanasys.com/ Nervana], December 21, 2015</ref>
==2015 ...==
* [[Volodymyr Mnih]], [[Koray Kavukcuoglu]], [[David Silver]], [[Andrei A. Rusu]], [[Joel Veness]], [[Marc G. Bellemare]], [[Alex Graves]], [[Martin Riedmiller]], [[Andreas K. Fidjeland]], [[Georg Ostrovski]], [[Stig Petersen]], [[Charles Beattie]], [[Amir Sadik]], [[Ioannis Antonoglou]], [[Helen King]], [[Dharshan Kumaran]], [[Daan Wierstra]], [[Shane Legg]], [[Demis Hassabis]] ('''2015'''). ''[http://www.nature.com/nature/journal/v518/n7540/abs/nature14236.html Human-level control through deep reinforcement learning]''. [https://en.wikipedia.org/wiki/Nature_%28journal%29 Nature], Vol. 518
* [[Volodymyr Mnih]], [[Adrià Puigdomènech Badia]], [[Mehdi Mirza]], [[Alex Graves]], [[Timothy Lillicrap]], [[Tim Harley]], [[David Silver]], [[Koray Kavukcuoglu]] ('''2016'''). ''Asynchronous Methods for Deep Reinforcement Learning''. [https://arxiv.org/abs/1602.01783 arXiv:1602.01783v2]
* [[Max Jaderberg]], [[Volodymyr Mnih]], [[Wojciech Marian Czarnecki]], [[Tom Schaul]], [[Joel Z. Leibo]], [[David Silver]], [[Koray Kavukcuoglu]] ('''2016'''). ''Reinforcement Learning with Unsupervised Auxiliary Tasks''. [https://arxiv.org/abs/1611.05397v1 arXiv:1611.05397v1]
* [[Hado van Hasselt]], [[Arthur Guez]], [[Matteo Hessel]], [[Volodymyr Mnih]], [[David Silver]] ('''2016'''). ''Learning values across many orders of magnitude''. [https://arxiv.org/abs/1602.07714 arXiv:1602.07714v2], [https://nips.cc/Conferences/2016/Schedule?type=Poster NIPS 2016]
=External Links=
* [https://www.cs.toronto.edu/~vmnih/ Vlad Mnih - Homepage]
* [https://scholar.google.com/citations?user=rLdfJ1gAAAAJ&hl=en Volodymyr Mnih - Google Scholar Citations]
=References=
<references />
'''[[People|Up one level]]'''
[[FILE:VolodymyrMnih.jpg|border|right|thumb|link=https://www.cs.toronto.edu/~vmnih/| Volodymyr Mnih <ref>[https://www.cs.toronto.edu/~vmnih/ Vlad Mnih - Homepage]</ref> ]]
'''Volodymyr Mnih''',<br/>
a Canadian research scientist at at [[Google]] [[DeepMind]] with expertise in [[Deep Learning|deep learning]], heading the team working on [[Neural Networks#Deep|deep Q-networks]] (DQN) mastering [https://en.wikipedia.org/wiki/Atari_Games Atari games] <ref>[https://propecta.com/googles-ai-can-beat-atari-games We’ll Never Win! Google’s AI Plays Atari - Propecta] by [https://www.linkedin.com/in/natedame/en Nate Dame], March 19, 2015</ref>. DQNs were tested with games such as [https://en.wikipedia.org/wiki/Pong Pong], [https://en.wikipedia.org/wiki/Space_Invaders Space Invaders], [https://en.wikipedia.org/wiki/Breakout_(video_game) Breakout] and [https://en.wikipedia.org/wiki/Seaquest_(video_game) Seaquest], receiving only the pixels and the game score as inputs, to surpass the performance of all previous algorithms and achieve a level comparable to that of a professional human games tester across a set of 49 games, using the same algorithm, network architecture and hyperparameters. Volodymyr Mnih holds a Ph.D. in [[Learning|machine learning]] from [[University of Toronto]] under supervision of [[Mathematician#GEHinton|Geoffrey E. Hinton]], and a Master's degree in computing science fro [[University of Alberta]] where his advisor was [[Csaba Szepesvári]].
=Selected Publications=
<ref>[http://dblp.uni-trier.de/pers/hd/m/Mnih:Volodymyr dblp: Volodymyr Mnih]</ref>
==2008==
* [[Volodymyr Mnih]] ('''2008'''). ''Efficient Stopping Rules''. Masters thesis, [[University of Alberta]], advisor: [[Csaba Szepesvári]], [https://www.cs.toronto.edu/~vmnih/docs/msc-thesis.pdf pdf]
==2010 ...==
* [[Volodymyr Mnih]], [[Mathematician#GEHinton|Geoffrey E. Hinton]] ('''2010'''). ''[http://link.springer.com/chapter/10.1007/978-3-642-15567-3_16 Learning to Detect Roads in High-Resolution Aerial Images]''. [http://dblp.uni-trier.de/db/conf/eccv/eccv2010-6.html#MnihH10 ECCV 2010]
* [[Volodymyr Mnih]] ('''2013'''). ''Machine Learning for Aerial Image Labeling''. Ph.D. thesis, [[University of Toronto]], advisor [[Mathematician#GEHinton|Geoffrey E. Hinton]], [https://www.cs.toronto.edu/~vmnih/docs/Mnih_Volodymyr_PhD_Thesis.pdf pdf]
* [[Volodymyr Mnih]], [[Koray Kavukcuoglu]], [[David Silver]], [[Alex Graves]], [[Ioannis Antonoglou]], [[Daan Wierstra]], [[Martin Riedmiller]] ('''2013'''). ''Playing Atari with Deep Reinforcement Learning''. [http://arxiv.org/abs/1312.5602 arXiv:1312.5602] <ref>[http://www.nervanasys.com/demystifying-deep-reinforcement-learning/ Demystifying Deep Reinforcement Learning] by [http://www.nervanasys.com/author/tambet/ Tambet Matiisen], [http://www.nervanasys.com/ Nervana], December 21, 2015</ref>
==2015 ...==
* [[Volodymyr Mnih]], [[Koray Kavukcuoglu]], [[David Silver]], [[Andrei A. Rusu]], [[Joel Veness]], [[Marc G. Bellemare]], [[Alex Graves]], [[Martin Riedmiller]], [[Andreas K. Fidjeland]], [[Georg Ostrovski]], [[Stig Petersen]], [[Charles Beattie]], [[Amir Sadik]], [[Ioannis Antonoglou]], [[Helen King]], [[Dharshan Kumaran]], [[Daan Wierstra]], [[Shane Legg]], [[Demis Hassabis]] ('''2015'''). ''[http://www.nature.com/nature/journal/v518/n7540/abs/nature14236.html Human-level control through deep reinforcement learning]''. [https://en.wikipedia.org/wiki/Nature_%28journal%29 Nature], Vol. 518
* [[Volodymyr Mnih]], [[Adrià Puigdomènech Badia]], [[Mehdi Mirza]], [[Alex Graves]], [[Timothy Lillicrap]], [[Tim Harley]], [[David Silver]], [[Koray Kavukcuoglu]] ('''2016'''). ''Asynchronous Methods for Deep Reinforcement Learning''. [https://arxiv.org/abs/1602.01783 arXiv:1602.01783v2]
* [[Max Jaderberg]], [[Volodymyr Mnih]], [[Wojciech Marian Czarnecki]], [[Tom Schaul]], [[Joel Z. Leibo]], [[David Silver]], [[Koray Kavukcuoglu]] ('''2016'''). ''Reinforcement Learning with Unsupervised Auxiliary Tasks''. [https://arxiv.org/abs/1611.05397v1 arXiv:1611.05397v1]
* [[Hado van Hasselt]], [[Arthur Guez]], [[Matteo Hessel]], [[Volodymyr Mnih]], [[David Silver]] ('''2016'''). ''Learning values across many orders of magnitude''. [https://arxiv.org/abs/1602.07714 arXiv:1602.07714v2], [https://nips.cc/Conferences/2016/Schedule?type=Poster NIPS 2016]
=External Links=
* [https://www.cs.toronto.edu/~vmnih/ Vlad Mnih - Homepage]
* [https://scholar.google.com/citations?user=rLdfJ1gAAAAJ&hl=en Volodymyr Mnih - Google Scholar Citations]
=References=
<references />
'''[[People|Up one level]]'''
