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D-Index & Metrics

Computer Science

D-Index
33
Citations
53953
World Ranking
12331
National Ranking
784

Overview

Volodymyr Mnih is affiliated with DeepMind in the United Kingdom. Their research primarily spans the field of Computer Science, with a focus on Artificial Intelligence and related subfields such as Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Computational Theory and Mathematics, and Management Science and Operations Research.

The scientist's main topics of work include Reinforcement Learning in Robotics, Domain Adaptation and Few-Shot Learning, Human Pose and Action Recognition, Adversarial Robustness in Machine Learning, Explainable Artificial Intelligence (XAI), Smart Grid Energy Management, and Adaptive Dynamic Programming Control.

Volodymyr Mnih has published in several venues, with the majority of papers appearing in arXiv (Cornell University). Additionally, there are contributions in the Proceedings of the AAAI Conference on Artificial Intelligence.

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence

Recent papers authored by or involving Volodymyr Mnih include:

  • "Q-Learning in enormous action spaces via amortized approximate maximization," 2020, arXiv (Cornell University)
  • "Q-Learning in enormous action spaces via amortized approximate maximization," 2020, arXiv (Cornell University)
  • "In-context Reinforcement Learning with Algorithm Distillation," 2022, arXiv (Cornell University)
  • "Relative Variational Intrinsic Control," 2021, Proceedings of the AAAI Conference on Artificial Intelligence
  • "Relative Variational Intrinsic Control," 2020, arXiv (Cornell University)

Frequent co-authors collaborating with Volodymyr Mnih include:

  • David Warde-Farley
  • Kate Baumli
  • Steven Hansen
  • Stephen Spencer
  • Maxime Gazeau

Best Publications

  • Human-level control through deep reinforcement learning

    Volodymyr Mnih;Koray Kavukcuoglu;David Silver;Andrei A. Rusu

  • Playing Atari with Deep Reinforcement Learning

    Volodymyr Mnih;Koray Kavukcuoglu;David Silver;Alex Graves

  • Asynchronous methods for deep reinforcement learning

    Volodymyr Mnih;Adrià Puigdomènech Badia;Mehdi Mirza;Alex Graves

  • Recurrent Models of Visual Attention

    Volodymyr Mnih;Nicolas Heess;Alex Graves;koray kavukcuoglu

  • Recurrent Models of Visual Attention

    Volodymyr Mnih;Nicolas Heess;Alex Graves;Koray Kavukcuoglu

  • Asynchronous Methods for Deep Reinforcement Learning

    Volodymyr Mnih;Adrià Puigdomènech Badia;Mehdi Mirza;Alex Graves

  • Reinforcement Learning with Unsupervised Auxiliary Tasks

    Max Jaderberg;Volodymyr Mnih;Wojciech Marian Czarnecki;Tom Schaul

  • Multiple Object Recognition with Visual Attention

    Jimmy Lei Ba;Volodymyr Mnih;Koray Kavukcuoglu

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

    Lasse Espeholt;Hubert Soyer;Remi Munos;Karen Simonyan

  • Machine learning for aerial image labeling

    Geoffrey Hinton;Volodymyr Mnih

  • Learning to detect roads in high-resolution aerial images

    Volodymyr Mnih;Geoffrey E. Hinton

  • Multiple Object Recognition with Visual Attention

    Jimmy Ba;Volodymyr Mnih;Koray Kavukcuoglu

  • Massively Parallel Methods for Deep Reinforcement Learning

    Arun Nair;Praveen Srinivasan;Sam Blackwell;Cagdas Alcicek

  • Sample Efficient Actor-Critic with Experience Replay.

    Ziyu Wang;Victor Bapst;Nicolas Heess;Volodymyr Mnih

  • Learning to Label Aerial Images from Noisy Data

    Volodymyr Mnih;Geoffrey E. Hinton

  • Learning by Playing - Solving Sparse Reward Tasks from Scratch

    Martin A. Riedmiller;Roland Hafner;Thomas Lampe;Michael Neunert

  • On deep generative models with applications to recognition

    Marc'Aurelio Ranzato;Joshua Susskind;Volodymyr Mnih;Geoffrey Hinton

  • Empirical Bernstein stopping

    Volodymyr Mnih;Csaba Szepesvári;Jean-Yves Audibert

  • Sample Efficient Actor-Critic with Experience Replay

    Ziyu Wang;Victor Bapst;Nicolas Heess;Volodymyr Mnih

  • Combining policy gradient and Q-learning

    Brendan O'Donoghue;Remi Munos;Koray Kavukcuoglu;Volodymyr Mnih

  • Using Fast Weights to Attend to the Recent Past

    Jimmy Ba;Geoffrey E. Hinton;Volodymyr Mnih;Joel Z. Leibo

  • Policy Distillation

    Andrei A. Rusu;Sergio Gomez Colmenarejo;Caglar Gulcehre;Guillaume Desjardins

  • Conditional restricted Boltzmann machines for structured output prediction

    Volodymyr Mnih;Hugo Larochelle;Geoffrey E. Hinton

  • The Uncertainty Bellman Equation and Exploration.

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

  • Unsupervised Learning of Object Keypoints for Perception and Control

    Tejas D. Kulkarni;Ankush Gupta;Catalin Ionescu;Sebastian Borgeaud

  • Learning by Playing - Solving Sparse Reward Tasks from Scratch

    Martin Riedmiller;Roland Hafner;Thomas Lampe;Michael Neunert

Frequent Co-Authors

Koray Kavukcuoglu
Koray Kavukcuoglu DeepMind (United Kingdom)
David Silver
David Silver DeepMind (United Kingdom)
Geoffrey E. Hinton
Geoffrey E. Hinton University of Toronto
Alex Graves
Alex Graves Google (United States)
Rémi Munos
Rémi Munos French Institute for Research in Computer Science and Automation - INRIA
Nicolas Heess
Nicolas Heess DeepMind (United Kingdom)
Martin Riedmiller
Martin Riedmiller DeepMind (United Kingdom)
Tom Schaul
Tom Schaul DeepMind (United Kingdom)
Joel Z. Leibo
Joel Z. Leibo DeepMind (United Kingdom)

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