Volodymyr Mnih

According to our database, Volodymyr Mnih authored at least 40 papers between 2006 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Vision-Language Models as a Source of Rewards.

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

CoRR, 2023

In-context Reinforcement Learning with Algorithm Distillation.

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

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Palm up: Playing in the Latent Manifold for Unsupervised Pretraining.

Hao Liu, Tom Zahavy, Volodymyr Mnih, Satinder Singh

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Learning more skills through optimistic exploration.

DJ Strouse, Kate Baumli, David Warde-Farley, Volodymyr Mnih, Steven Stenberg Hansen

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Wasserstein Distance Maximizing Intrinsic Control.

Ishan Durugkar, Steven Hansen, Stephen Spencer, Volodymyr Mnih

CoRR, 2021

Discovering Diverse Nearly Optimal Policies withSuccessor Features.

Tom Zahavy, Brendan O'Donoghue, André Barreto, Volodymyr Mnih, Sebastian Flennerhag, Satinder Singh

CoRR, 2021

Entropic Desired Dynamics for Intrinsic Control.

Steven Hansen, Guillaume Desjardins, Kate Baumli, David Warde-Farley, Nicolas Heess, Simon Osindero, Volodymyr Mnih

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Relative Variational Intrinsic Control.

Kate Baumli, David Warde-Farley, Steven Hansen, Volodymyr Mnih

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Q-Learning in enormous action spaces via amortized approximate maximization.

Tom Van de Wiele, David Warde-Farley, Andriy Mnih, Volodymyr Mnih

CoRR, 2020

Fast Task Inference with Variational Intrinsic Successor Features.

Steven Hansen, Will Dabney, André Barreto, David Warde-Farley, Tom Van de Wiele, Volodymyr Mnih

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Unsupervised Learning of Object Keypoints for Perception and Control.

Tejas D. Kulkarni, Ankush Gupta, Catalin Ionescu, Sebastian Borgeaud, Malcolm Reynolds, Andrew Zisserman, Volodymyr Mnih

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Unsupervised Control Through Non-Parametric Discriminative Rewards.

David Warde-Farley, Tom Van de Wiele, Tejas D. Kulkarni, Catalin Ionescu, Steven Hansen, Volodymyr Mnih

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

Learning by Playing Solving Sparse Reward Tasks from Scratch.

Martin A. Riedmiller, Roland Hafner, Thomas Lampe, Michael Neunert, Jonas Degrave, Tom Van de Wiele, Vlad Mnih, Nicolas Heess, Jost Tobias Springenberg

Proceedings of the 35th International Conference on Machine Learning, 2018

The Uncertainty Bellman Equation and Exploration.

Brendan O'Donoghue, Ian Osband, Rémi Munos, Volodymyr Mnih

Proceedings of the 35th International Conference on Machine Learning, 2018

IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures.

Lasse Espeholt, Hubert Soyer, Rémi Munos, Karen Simonyan, Volodymyr Mnih, Tom Ward, Yotam Doron, Vlad Firoiu, Tim Harley, Iain Dunning, Shane Legg, Koray Kavukcuoglu

Proceedings of the 35th International Conference on Machine Learning, 2018

Noisy Networks For Exploration.

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

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Noisy Networks for Exploration.

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

CoRR, 2017

Combining policy gradient and Q-learning.

Brendan O'Donoghue, Rémi Munos, Koray Kavukcuoglu, Volodymyr Mnih

Proceedings of the 5th International Conference on Learning Representations, 2017

Reinforcement Learning with Unsupervised Auxiliary Tasks.

Max Jaderberg, Volodymyr Mnih, Wojciech Marian Czarnecki, Tom Schaul, Joel Z. Leibo, David Silver, Koray Kavukcuoglu

Proceedings of the 5th International Conference on Learning Representations, 2017

Sample Efficient Actor-Critic with Experience Replay.

Ziyu Wang, Victor Bapst, Nicolas Heess, Volodymyr Mnih, Rémi Munos, Koray Kavukcuoglu, Nando de Freitas

Proceedings of the 5th International Conference on Learning Representations, 2017

2016

Policy Distillation.

Andrei A. Rusu, Sergio Gomez Colmenarejo, Çaglar Gülçehre, Guillaume Desjardins, James Kirkpatrick, Razvan Pascanu, Volodymyr Mnih, Koray Kavukcuoglu, Raia Hadsell

Proceedings of the 4th International Conference on Learning Representations, 2016

PGQ: Combining policy gradient and Q-learning.

Brendan O'Donoghue, Rémi Munos, Koray Kavukcuoglu, Volodymyr Mnih

CoRR, 2016

Strategic Attentive Writer for Learning Macro-Actions.

Alexander Vezhnevets, Volodymyr Mnih, Simon Osindero, Alex Graves, Oriol Vinyals, John P. Agapiou, Koray Kavukcuoglu

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Learning values across many orders of magnitude.

Hado van Hasselt, Arthur Guez, Matteo Hessel, Volodymyr Mnih, David Silver

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Using Fast Weights to Attend to the Recent Past.

Jimmy Ba, Geoffrey E. Hinton, Volodymyr Mnih, Joel Z. Leibo, Catalin Ionescu

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Asynchronous Methods for Deep Reinforcement Learning.

Volodymyr Mnih, Adrià Puigdomènech Badia, Mehdi Mirza, Alex Graves, Timothy P. Lillicrap, Tim Harley, David Silver, Koray Kavukcuoglu

Proceedings of the 33nd International Conference on Machine Learning, 2016

2015

Human-level control through deep reinforcement learning.

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

Nat., 2015

Massively Parallel Methods for Deep Reinforcement Learning.

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

CoRR, 2015

Multiple Object Recognition with Visual Attention.

Jimmy Ba, Volodymyr Mnih, Koray Kavukcuoglu

Proceedings of the 3rd International Conference on Learning Representations, 2015

2014

Recurrent Models of Visual Attention.

Volodymyr Mnih, Nicolas Heess, Alex Graves, Koray Kavukcuoglu

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

2013

Machine Learning for Aerial Image Labeling.

Volodymyr Mnih

PhD thesis, 2013

Modeling Natural Images Using Gated MRFs.

Marc'Aurelio Ranzato, Volodymyr Mnih, Joshua M. Susskind, Geoffrey E. Hinton

IEEE Trans. Pattern Anal. Mach. Intell., 2013

Playing Atari with Deep Reinforcement Learning.

Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, Martin A. Riedmiller

CoRR, 2013

2012

Learning to Label Aerial Images from Noisy Data.

Volodymyr Mnih, Geoffrey E. Hinton

Proceedings of the 29th International Conference on Machine Learning, 2012

2011

Conditional Restricted Boltzmann Machines for Structured Output Prediction.

Volodymyr Mnih, Hugo Larochelle, Geoffrey E. Hinton

Proceedings of the UAI 2011, 2011

On deep generative models with applications to recognition.

Marc'Aurelio Ranzato, Joshua M. Susskind, Volodymyr Mnih, Geoffrey E. Hinton

Proceedings of the 24th IEEE Conference on Computer Vision and Pattern Recognition, 2011

2010

Generating more realistic images using gated MRF's.

Marc'Aurelio Ranzato, Volodymyr Mnih, Geoffrey E. Hinton

Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

Learning to Detect Roads in High-Resolution Aerial Images.

Volodymyr Mnih, Geoffrey E. Hinton

Proceedings of the Computer Vision - ECCV 2010, 2010

2008

Empirical Bernstein stopping.

Volodymyr Mnih, Csaba Szepesvári, Jean-Yves Audibert

Proceedings of the Machine Learning, 2008

2006

Topological map learning from outdoor image sequences.

Xuming He, Richard S. Zemel, Volodymyr Mnih

J. Field Robotics, 2006