Topological map learning from outdoor image sequences. - Xuming He 001; Richard S. Zemel; Volodymyr Mnih (J. Field Robotics, 2006)

Empirical Bernstein stopping. - Volodymyr Mnih; Csaba Szepesvári; Jean-Yves Audibert (ICML, 2008)

Learning to Detect Roads in High-Resolution Aerial Images. - Volodymyr Mnih; Geoffrey E. Hinton (ECCV (6), 2010)

Generating more realistic images using gated MRF's. - Marc'Aurelio Ranzato; Volodymyr Mnih; Geoffrey E. Hinton (NIPS, 2010)

On deep generative models with applications to recognition. - Marc'Aurelio Ranzato; Joshua M. Susskind; Volodymyr Mnih; Geoffrey E. Hinton (CVPR, 2011)

Conditional Restricted Boltzmann Machines for Structured Output Prediction. - Volodymyr Mnih; Hugo Larochelle; Geoffrey E. Hinton (UAI, 2011)

Learning to Label Aerial Images from Noisy Data. - Volodymyr Mnih; Geoffrey E. Hinton (ICML, 2012)

Playing Atari with Deep Reinforcement Learning. - Volodymyr Mnih; Koray Kavukcuoglu; David Silver; Alex Graves; Ioannis Antonoglou; Daan Wierstra; Martin A. Riedmiller (CoRR, 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)

Machine Learning for Aerial Image Labeling. - Volodymyr Mnih (2013)

Recurrent Models of Visual Attention. - Volodymyr Mnih; Nicolas Heess; Alex Graves; Koray Kavukcuoglu (NIPS, 2014)

Multiple Object Recognition with Visual Attention. - Jimmy Ba; Volodymyr Mnih; Koray Kavukcuoglu (ICLR (Poster), 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; et al. (CoRR, 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; et al. (Nat., 2015)

Asynchronous Methods for Deep Reinforcement Learning. - Volodymyr Mnih; Adrià Puigdomènech Badia; Mehdi Mirza; Alex Graves; Timothy P. Lillicrap; Tim Harley; David Silver; Koray Kavukcuoglu (ICML, 2016)

Using Fast Weights to Attend to the Recent Past. - Jimmy Ba; Geoffrey E. Hinton; Volodymyr Mnih; Joel Z. Leibo; Catalin Ionescu (NIPS, 2016)

Learning values across many orders of magnitude. - Hado van Hasselt; Arthur Guez; Matteo Hessel; Volodymyr Mnih; David Silver (NIPS, 2016)

Strategic Attentive Writer for Learning Macro-Actions. - Alexander Vezhnevets; Volodymyr Mnih; Simon Osindero; Alex Graves; Oriol Vinyals; John P. Agapiou; Koray Kavukcuoglu (NIPS, 2016)

PGQ: Combining policy gradient and Q-learning. - Brendan O'Donoghue; Rémi Munos; Koray Kavukcuoglu; Volodymyr Mnih (CoRR, 2016)

Policy Distillation. - Andrei A. Rusu; Sergio Gomez Colmenarejo; Çaglar Gülçehre; Guillaume Desjardins; James Kirkpatrick; Razvan Pascanu; Volodymyr Mnih; Koray Kavukcuoglu; Raia Hadsell (ICLR (Poster), 2016)

Sample Efficient Actor-Critic with Experience Replay. - Ziyu Wang 001; Victor Bapst; Nicolas Heess; Volodymyr Mnih; Rémi Munos; Koray Kavukcuoglu; Nando de Freitas (ICLR (Poster), 2017)

Reinforcement Learning with Unsupervised Auxiliary Tasks. - Max Jaderberg; Volodymyr Mnih; Wojciech Marian Czarnecki; Tom Schaul; Joel Z. Leibo; David Silver; Koray Kavukcuoglu (ICLR, 2017)

Combining policy gradient and Q-learning. - Brendan O'Donoghue; Rémi Munos; Koray Kavukcuoglu; Volodymyr Mnih (ICLR (Poster), 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)

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; et al. (ICLR (Poster), 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 (ICML, 2018)

The Uncertainty Bellman Equation and Exploration. - Brendan O'Donoghue; Ian Osband; Rémi Munos; Volodymyr Mnih (ICML, 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 (ICML, 2018)

Unsupervised Control Through Non-Parametric Discriminative Rewards. - David Warde-Farley; Tom Van de Wiele; Tejas D. Kulkarni; Catalin Ionescu; Steven Hansen 001; Volodymyr Mnih (ICLR (Poster), 2019)

Unsupervised Learning of Object Keypoints for Perception and Control. - Tejas D. Kulkarni; Ankush Gupta 001; Catalin Ionescu; Sebastian Borgeaud; Malcolm Reynolds; Andrew Zisserman; Volodymyr Mnih (NeurIPS, 2019)

Fast Task Inference with Variational Intrinsic Successor Features. - Steven Hansen 001; Will Dabney; André Barreto 001; David Warde-Farley; Tom Van de Wiele; Volodymyr Mnih (ICLR, 2020)

Q-Learning in enormous action spaces via amortized approximate maximization. - Tom Van de Wiele; David Warde-Farley; Andriy Mnih; Volodymyr Mnih (CoRR, 2020)

Relative Variational Intrinsic Control. - Kate Baumli; David Warde-Farley; Steven Hansen 001; Volodymyr Mnih (AAAI, 2021)

Entropic Desired Dynamics for Intrinsic Control. - Steven Hansen 001; Guillaume Desjardins; Kate Baumli; David Warde-Farley; Nicolas Heess; Simon Osindero; Volodymyr Mnih (NeurIPS, 2021)

Discovering Diverse Nearly Optimal Policies withSuccessor Features. - Tom Zahavy; Brendan O'Donoghue; André Barreto 001; Volodymyr Mnih; Sebastian Flennerhag; Satinder Singh 001 (CoRR, 2021)

Wasserstein Distance Maximizing Intrinsic Control. - Ishan Durugkar; Steven Hansen 001; Stephen Spencer; Volodymyr Mnih (CoRR, 2021)

Learning more skills through optimistic exploration. - DJ Strouse; Kate Baumli; David Warde-Farley; Volodymyr Mnih; Steven Stenberg Hansen (ICLR, 2022)

Palm up: Playing in the Latent Manifold for Unsupervised Pretraining. - Hao Liu 055; Tom Zahavy; Volodymyr Mnih; Satinder Singh 001 (NeurIPS, 2022)

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 Brooks; Maxime Gazeau; Himanshu Sahni; et al. (ICLR, 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; et al. (CoRR, 2023)

LMAct: A Benchmark for In-Context Imitation Learning with Long Multimodal Demonstrations. - Anian Ruoss; Fabio Pardo; Harris Chan; Bonnie Li; Volodymyr Mnih; Tim Genewein (CoRR, 2024)

ElasticTok: Adaptive Tokenization for Image and Video. - Wilson Yan; Volodymyr Mnih; Aleksandra Faust; Matei Zaharia; Pieter Abbeel; Hao Liu 055 (ICLR, 2025)