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Computer Science
UK
2025

D-Index & Metrics

Computer Science

D-Index
91
Citations
181384
World Ranking
553
National Ranking
34

Research.com Recognitions

  • 2025 - Research.com Computer Science in United Kingdom Leader Award
  • 2022 - Research.com Computer Science in United Kingdom Leader Award

Overview

Oriol Vinyals is a researcher affiliated with DeepMind in the United Kingdom. Their primary field of study is Computer Science, with a significant focus on Artificial Intelligence, as reflected in their extensive publication record. Their work spans several subfields including Computer Vision and Pattern Recognition, Molecular Biology, Materials Chemistry, and General Health Professions.

The main topics covered in their research include Topic Modeling, Domain Adaptation and Few-Shot Learning, Multimodal Machine Learning Applications, Natural Language Processing Techniques, Advanced Graph Neural Networks, Machine Learning and Data Classification, and Reinforcement Learning in Robotics.

Some of Oriol Vinyals' recent papers include:

  • Highly accurate protein structure prediction with AlphaFold, 2021, Nature
  • Understanding the Impact of Value Selection Heuristics in Scheduling Problems, 2025, arXiv (Cornell University)
  • Understanding deep learning (still) requires rethinking generalization, 2021, Communications of the ACM
  • Flamingo: a Visual Language Model for Few-Shot Learning, 2022, arXiv (Cornell University)
  • Emergent Abilities of Large Language Models, 2022, arXiv (Cornell University)

Frequent co-authors collaborating with Oriol Vinyals include Sebastian Borgeaud, Koray Kavukcuoglu, Pushmeet Kohli, Yujia Li, and Karen Simonyan.

They have published extensively in multiple venues, with numerous papers appearing in arXiv (Cornell University). Other frequent publication venues include Nature, Science, the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), and Communications of the ACM.

In addition to journal and conference publications, Oriol Vinyals has contributed to book publications, including a work released by RAND Corporation eBooks titled "Exploring the Feasibility and Utility of Machine Learning-Assisted Command and Control: Volume 2, Supporting Technical Analysis" published in 2021.

Best Publications

  • Highly accurate protein structure prediction with AlphaFold

    John M. Jumper;Richard O. Evans;Alexander Pritzel;Tim Green

  • Distilling the Knowledge in a Neural Network

    Geoffrey E. Hinton;Oriol Vinyals;Jeffrey Dean

  • TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

    Martín Abadi;Ashish Agarwal;Paul Barham;Eugene Brevdo

  • Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

    Yonghui Wu;Mike Schuster;Zhifeng Chen;Quoc V. Le

  • Show and tell: A neural image caption generator

    Oriol Vinyals;Alexander Toshev;Samy Bengio;Dumitru Erhan

  • Understanding deep learning (still) requires rethinking generalization

    Chiyuan Zhang;Samy Bengio;Moritz Hardt;Benjamin Recht

  • Representation Learning with Contrastive Predictive Coding

    Aaron van den Oord;Yazhe Li;Oriol Vinyals

  • Matching networks for one shot learning

    Oriol Vinyals;Charles Blundell;Timothy Lillicrap;Koray Kavukcuoglu

  • WaveNet: A Generative Model for Raw Audio

    Aäron van den Oord;Sander Dieleman;Heiga Zen;Karen Simonyan

  • DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition

    Jeff Donahue;Yangqing Jia;Oriol Vinyals;Judy Hoffman

  • Neural Discrete Representation Learning

    Aaron van den Oord;Oriol Vinyals;koray kavukcuoglu

  • Neural Message Passing for Quantum Chemistry

    Justin Gilmer;Samuel S. Schoenholz;Patrick F. Riley;Oriol Vinyals

  • Grandmaster level in StarCraft II using multi-agent reinforcement learning.

    Oriol Vinyals;Igor Babuschkin;Wojciech M. Czarnecki;Michaël Mathieu

  • Understanding deep learning requires rethinking generalization.

    Chiyuan Zhang;Samy Bengio;Moritz Hardt;Benjamin Recht

  • Relational inductive biases, deep learning, and graph networks

    Peter W. Battaglia;Jessica B. Hamrick;Victor Bapst;Alvaro Sanchez-Gonzalez

  • Beyond short snippets: Deep networks for video classification

    Joe Yue-Hei Ng;Matthew Hausknecht;Sudheendra Vijayanarasimhan;Oriol Vinyals

  • Listen, attend and spell: A neural network for large vocabulary conversational speech recognition

    William Chan;Navdeep Jaitly;Quoc Le;Oriol Vinyals

  • A Neural Conversational Model

    Oriol Vinyals;Quoc V. Le

  • Generating Sentences from a Continuous Space

    Samuel R. Bowman;Luke Vilnis;Oriol Vinyals;Andrew M. Dai

  • Conditional image generation with PixelCNN decoders

    Aäron van den Oord;Nal Kalchbrenner;Oriol Vinyals;Lasse Espeholt

  • Scheduled sampling for sequence prediction with recurrent Neural networks

    Samy Bengio;Oriol Vinyals;Navdeep Jaitly;Noam Shazeer

Frequent Co-Authors

Koray Kavukcuoglu
Koray Kavukcuoglu DeepMind (United Kingdom)
Aaron van den Oord
Aaron van den Oord Google (United States)
Razvan Pascanu
Razvan Pascanu DeepMind (United Kingdom)
Samy Bengio
Samy Bengio Apple (United States)
Karen Simonyan
Karen Simonyan DeepMind (United Kingdom)
Daan Wierstra
Daan Wierstra DeepMind (United Kingdom)
Alex Graves
Alex Graves Google (United States)
Peter W. Battaglia
Peter W. Battaglia DeepMind (United Kingdom)
Quoc V. Le
Quoc V. Le Google (United States)
Navdeep Jaitly
Navdeep Jaitly Google (United States)

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