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2020 – today
- 2024
[i26]Wilson Yan, Matei Zaharia, Volodymyr Mnih, Pieter Abbeel, Aleksandra Faust, Hao Liu:
ElasticTok: Adaptive Tokenization for Image and Video. CoRR abs/2410.08368 (2024)- 2023
- [c28]Michael Laskin, Luyu Wang, Junhyuk Oh, Emilio Parisotto, Stephen Spencer, Richie Steigerwald, DJ Strouse, Steven Stenberg Hansen, Angelos Filos, Ethan A. Brooks, Maxime Gazeau, Himanshu Sahni, Satinder Singh, Volodymyr Mnih:
In-context Reinforcement Learning with Algorithm Distillation. ICLR 2023 - [i25]Kate Baumli, Satinder Baveja, Feryal M. P. Behbahani, Harris Chan, Gheorghe Comanici, Sebastian Flennerhag, Maxime Gazeau, Kristian Holsheimer, Dan Horgan, Michael Laskin, Clare Lyle, Hussain Masoom, Kay McKinney, Volodymyr Mnih, Alexander Neitz, Fabio Pardo, Jack Parker-Holder, John Quan, Tim Rocktäschel, Himanshu Sahni, Tom Schaul, Yannick Schroecker, Stephen Spencer, Richie Steigerwald, Luyu Wang, Lei Zhang:
Vision-Language Models as a Source of Rewards. CoRR abs/2312.09187 (2023) - 2022
- [c27]DJ Strouse, Kate Baumli, David Warde-Farley, Volodymyr Mnih, Steven Stenberg Hansen:
Learning more skills through optimistic exploration. ICLR 2022 - [c26]Hao Liu, Tom Zahavy, Volodymyr Mnih, Satinder Singh:
Palm up: Playing in the Latent Manifold for Unsupervised Pretraining. NeurIPS 2022 - [i24]Hao Liu, Tom Zahavy, Volodymyr Mnih, Satinder Singh:
Palm up: Playing in the Latent Manifold for Unsupervised Pretraining. CoRR abs/2210.10913 (2022) - [i23]Michael Laskin, Luyu Wang, Junhyuk Oh, Emilio Parisotto, Stephen Spencer, Richie Steigerwald, DJ Strouse, Steven Hansen, Angelos Filos, Ethan A. Brooks, Maxime Gazeau, Himanshu Sahni, Satinder Singh, Volodymyr Mnih:
In-context Reinforcement Learning with Algorithm Distillation. CoRR abs/2210.14215 (2022) - 2021
- [c25]Kate Baumli, David Warde-Farley, Steven Hansen, Volodymyr Mnih:
Relative Variational Intrinsic Control. AAAI 2021: 6732-6740 - [c24]Steven Hansen, Guillaume Desjardins, Kate Baumli, David Warde-Farley, Nicolas Heess, Simon Osindero, Volodymyr Mnih:
Entropic Desired Dynamics for Intrinsic Control. NeurIPS 2021: 11436-11448 - [i22]Tom Zahavy, Brendan O'Donoghue, André Barreto, Volodymyr Mnih, Sebastian Flennerhag, Satinder Singh:
Discovering Diverse Nearly Optimal Policies withSuccessor Features. CoRR abs/2106.00669 (2021) - [i21]DJ Strouse, Kate Baumli, David Warde-Farley, Vlad Mnih, Steven Hansen:
Learning more skills through optimistic exploration. CoRR abs/2107.14226 (2021) - [i20]Ishan Durugkar, Steven Hansen, Stephen Spencer, Volodymyr Mnih:
Wasserstein Distance Maximizing Intrinsic Control. CoRR abs/2110.15331 (2021) - 2020
- [c23]Steven Hansen, Will Dabney, André Barreto, David Warde-Farley, Tom Van de Wiele, Volodymyr Mnih:
Fast Task Inference with Variational Intrinsic Successor Features. ICLR 2020 - [i19]Tom Van de Wiele, David Warde-Farley, Andriy Mnih, Volodymyr Mnih:
Q-Learning in enormous action spaces via amortized approximate maximization. CoRR abs/2001.08116 (2020) - [i18]Kate Baumli, David Warde-Farley, Steven Hansen, Volodymyr Mnih:
Relative Variational Intrinsic Control. CoRR abs/2012.07827 (2020)
2010 – 2019
- 2019
- [c22]David Warde-Farley, Tom Van de Wiele, Tejas D. Kulkarni, Catalin Ionescu, Steven Hansen, Volodymyr Mnih:
Unsupervised Control Through Non-Parametric Discriminative Rewards. ICLR (Poster) 2019 - [c21]Tejas D. Kulkarni, Ankush Gupta, Catalin Ionescu, Sebastian Borgeaud, Malcolm Reynolds, Andrew Zisserman, Volodymyr Mnih:
Unsupervised Learning of Object Keypoints for Perception and Control. NeurIPS 2019: 10723-10733 - [i17]Steven Hansen, Will Dabney, André Barreto, Tom Van de Wiele, David Warde-Farley, Volodymyr Mnih:
Fast Task Inference with Variational Intrinsic Successor Features. CoRR abs/1906.05030 (2019) - [i16]Tejas D. Kulkarni, Ankush Gupta, Catalin Ionescu, Sebastian Borgeaud, Malcolm Reynolds, Andrew Zisserman, Volodymyr Mnih:
Unsupervised Learning of Object Keypoints for Perception and Control. CoRR abs/1906.11883 (2019) - 2018
- [c20]Meire Fortunato, Mohammad Gheshlaghi Azar, Bilal Piot, Jacob Menick, Matteo Hessel, Ian Osband, Alex Graves, Volodymyr Mnih, Rémi Munos, Demis Hassabis, Olivier Pietquin, Charles Blundell, Shane Legg:
Noisy Networks For Exploration. ICLR (Poster) 2018 - [c19]Lasse Espeholt, Hubert Soyer, Rémi Munos, Karen Simonyan, Volodymyr Mnih, Tom Ward, Yotam Doron, Vlad Firoiu, Tim Harley, Iain Dunning, Shane Legg, Koray Kavukcuoglu:
IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures. ICML 2018: 1406-1415 - [c18]Brendan O'Donoghue, Ian Osband, Rémi Munos, Volodymyr Mnih:
The Uncertainty Bellman Equation and Exploration. ICML 2018: 3836-3845 - [c17]Martin A. Riedmiller, Roland Hafner, Thomas Lampe, Michael Neunert, Jonas Degrave, Tom Van de Wiele, Vlad Mnih, Nicolas Heess, Jost Tobias Springenberg:
Learning by Playing Solving Sparse Reward Tasks from Scratch. ICML 2018: 4341-4350 - [i15]Lasse Espeholt, Hubert Soyer, Rémi Munos, Karen Simonyan, Volodymyr Mnih, Tom Ward, Yotam Doron, Vlad Firoiu, Tim Harley, Iain Dunning, Shane Legg, Koray Kavukcuoglu:
IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures. CoRR abs/1802.01561 (2018) - [i14]Martin A. Riedmiller, Roland Hafner, Thomas Lampe, Michael Neunert, Jonas Degrave, Tom Van de Wiele, Volodymyr Mnih, Nicolas Heess, Jost Tobias Springenberg:
Learning by Playing - Solving Sparse Reward Tasks from Scratch. CoRR abs/1802.10567 (2018) - [i13]David Warde-Farley, Tom Van de Wiele, Tejas D. Kulkarni, Catalin Ionescu, Steven Hansen, Volodymyr Mnih:
Unsupervised Control Through Non-Parametric Discriminative Rewards. CoRR abs/1811.11359 (2018) - 2017
- [c16]Ziyu Wang, Victor Bapst, Nicolas Heess, Volodymyr Mnih, Rémi Munos, Koray Kavukcuoglu, Nando de Freitas:
Sample Efficient Actor-Critic with Experience Replay. ICLR (Poster) 2017 - [c15]Max Jaderberg, Volodymyr Mnih, Wojciech Marian Czarnecki, Tom Schaul, Joel Z. Leibo
, David Silver, Koray Kavukcuoglu:
Reinforcement Learning with Unsupervised Auxiliary Tasks. ICLR 2017 - [c14]Brendan O'Donoghue, Rémi Munos, Koray Kavukcuoglu, Volodymyr Mnih:
Combining policy gradient and Q-learning. ICLR (Poster) 2017 - [i12]Meire Fortunato, Mohammad Gheshlaghi Azar, Bilal Piot, Jacob Menick, Ian Osband, Alex Graves, Vlad Mnih, Rémi Munos, Demis Hassabis, Olivier Pietquin, Charles Blundell, Shane Legg:
Noisy Networks for Exploration. CoRR abs/1706.10295 (2017) - [i11]Brendan O'Donoghue, Ian Osband, Rémi Munos, Volodymyr Mnih:
The Uncertainty Bellman Equation and Exploration. CoRR abs/1709.05380 (2017) - 2016
- [c13]Volodymyr Mnih, Adrià Puigdomènech Badia, Mehdi Mirza, Alex Graves, Timothy P. Lillicrap, Tim Harley, David Silver, Koray Kavukcuoglu:
Asynchronous Methods for Deep Reinforcement Learning. ICML 2016: 1928-1937 - [c12]Alexander Vezhnevets, Volodymyr Mnih, Simon Osindero, Alex Graves, Oriol Vinyals, John P. Agapiou, Koray Kavukcuoglu:
Strategic Attentive Writer for Learning Macro-Actions. NIPS 2016: 3486-3494 - [c11]Hado van Hasselt, Arthur Guez, Matteo Hessel, Volodymyr Mnih, David Silver:
Learning values across many orders of magnitude. NIPS 2016: 4287-4295 - [c10]Jimmy Ba, Geoffrey E. Hinton, Volodymyr Mnih, Joel Z. Leibo, Catalin Ionescu:
Using Fast Weights to Attend to the Recent Past. NIPS 2016: 4331-4339 - [c9]Andrei A. Rusu, Sergio Gomez Colmenarejo, Çaglar Gülçehre, Guillaume Desjardins, James Kirkpatrick, Razvan Pascanu, Volodymyr Mnih, Koray Kavukcuoglu, Raia Hadsell:
Policy Distillation. ICLR (Poster) 2016 - [i10]Volodymyr Mnih, Adrià Puigdomènech Badia, Mehdi Mirza, Alex Graves, Timothy P. Lillicrap, Tim Harley, David Silver, Koray Kavukcuoglu:
Asynchronous Methods for Deep Reinforcement Learning. CoRR abs/1602.01783 (2016) - [i9]Alexander Vezhnevets, Volodymyr Mnih, John P. Agapiou, Simon Osindero, Alex Graves, Oriol Vinyals, Koray Kavukcuoglu:
Strategic Attentive Writer for Learning Macro-Actions. CoRR abs/1606.04695 (2016) - [i8]Jimmy Ba, Geoffrey E. Hinton, Volodymyr Mnih, Joel Z. Leibo, Catalin Ionescu:
Using Fast Weights to Attend to the Recent Past. CoRR abs/1610.06258 (2016) - [i7]Ziyu Wang, Victor Bapst, Nicolas Heess, Volodymyr Mnih, Rémi Munos, Koray Kavukcuoglu, Nando de Freitas:
Sample Efficient Actor-Critic with Experience Replay. CoRR abs/1611.01224 (2016) - [i6]Brendan O'Donoghue, Rémi Munos, Koray Kavukcuoglu, Volodymyr Mnih:
PGQ: Combining policy gradient and Q-learning. CoRR abs/1611.01626 (2016) - [i5]Max Jaderberg, Volodymyr Mnih, Wojciech Marian Czarnecki, Tom Schaul, Joel Z. Leibo, David Silver, Koray Kavukcuoglu:
Reinforcement Learning with Unsupervised Auxiliary Tasks. CoRR abs/1611.05397 (2016) - 2015
- [j3]Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin A. Riedmiller, Andreas Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, Demis Hassabis:
Human-level control through deep reinforcement learning. Nat. 518(7540): 529-533 (2015) - [c8]Jimmy Ba, Volodymyr Mnih, Koray Kavukcuoglu:
Multiple Object Recognition with Visual Attention. ICLR (Poster) 2015 - [i4]Arun Nair, Praveen Srinivasan, Sam Blackwell, Cagdas Alcicek, Rory Fearon, Alessandro De Maria, Vedavyas Panneershelvam, Mustafa Suleyman, Charles Beattie, Stig Petersen, Shane Legg, Volodymyr Mnih, Koray Kavukcuoglu, David Silver:
Massively Parallel Methods for Deep Reinforcement Learning. CoRR abs/1507.04296 (2015) - 2014
- [c7]Volodymyr Mnih, Nicolas Heess, Alex Graves, Koray Kavukcuoglu:
Recurrent Models of Visual Attention. NIPS 2014: 2204-2212 - [i3]Volodymyr Mnih, Nicolas Heess, Alex Graves, Koray Kavukcuoglu:
Recurrent Models of Visual Attention. CoRR abs/1406.6247 (2014) - 2013
- [b1]Volodymyr Mnih:
Machine Learning for Aerial Image Labeling. University of Toronto, Canada, 2013 - [j2]Marc'Aurelio Ranzato, Volodymyr Mnih, Joshua M. Susskind, Geoffrey E. Hinton:
Modeling Natural Images Using Gated MRFs. IEEE Trans. Pattern Anal. Mach. Intell. 35(9): 2206-2222 (2013) - [i2]Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, Martin A. Riedmiller:
Playing Atari with Deep Reinforcement Learning. CoRR abs/1312.5602 (2013) - 2012
- [c6]Volodymyr Mnih, Geoffrey E. Hinton:
Learning to Label Aerial Images from Noisy Data. ICML 2012 - [i1]Volodymyr Mnih, Hugo Larochelle, Geoffrey E. Hinton:
Conditional Restricted Boltzmann Machines for Structured Output Prediction. CoRR abs/1202.3748 (2012) - 2011
- [c5]Marc'Aurelio Ranzato, Joshua M. Susskind, Volodymyr Mnih, Geoffrey E. Hinton:
On deep generative models with applications to recognition. CVPR 2011: 2857-2864 - [c4]Volodymyr Mnih, Hugo Larochelle, Geoffrey E. Hinton:
Conditional Restricted Boltzmann Machines for Structured Output Prediction. UAI 2011: 514-522 - 2010
- [c3]Volodymyr Mnih, Geoffrey E. Hinton:
Learning to Detect Roads in High-Resolution Aerial Images. ECCV (6) 2010: 210-223 - [c2]Marc'Aurelio Ranzato, Volodymyr Mnih, Geoffrey E. Hinton:
Generating more realistic images using gated MRF's. NIPS 2010: 2002-2010
2000 – 2009
- 2008
- [c1]Volodymyr Mnih, Csaba Szepesvári, Jean-Yves Audibert:
Empirical Bernstein stopping. ICML 2008: 672-679 - 2006
- [j1]Xuming He, Richard S. Zemel, Volodymyr Mnih:
Topological map learning from outdoor image sequences. J. Field Robotics 23(11-12): 1091-1104 (2006)
Coauthor Index
aka: Steven Stenberg Hansen
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