O
Oriol Vinyals
Researcher at Google
Publications - 218
Citations - 121048
Oriol Vinyals is an academic researcher from Google. The author has contributed to research in topics: Artificial neural network & Reinforcement learning. The author has an hindex of 84, co-authored 200 publications receiving 82365 citations. Previous affiliations of Oriol Vinyals include University of California, San Diego & University of California, Berkeley.
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Proceedings Article
Neural Message Passing for Quantum Chemistry
TL;DR: The Message Passing Neural Networks (MPNNs) as mentioned in this paper are a generalization of the message passing algorithm and aggregation procedure to compute a function of their entire input graph, and have been shown to achieve state-of-the-art results on an important molecular property prediction benchmark.
Journal ArticleDOI
Grandmaster level in StarCraft II using multi-agent reinforcement learning.
Oriol Vinyals,Igor Babuschkin,Wojciech Marian Czarnecki,Michael Mathieu,Andrew Dudzik,Junyoung Chung,David H. Choi,Richard E. Powell,Timo Ewalds,Petko Georgiev,Junhyuk Oh,Dan Horgan,Manuel Kroiss,Ivo Danihelka,Aja Huang,Laurent Sifre,Trevor Cai,John P. Agapiou,Max Jaderberg,Alexander Vezhnevets,Rémi Leblond,Tobias Pohlen,Valentin Dalibard,David Budden,Yury Sulsky,James Molloy,Tom Le Paine,Caglar Gulcehre,Ziyu Wang,Tobias Pfaff,Yuhuai Wu,Roman Ring,Dani Yogatama,Dario Wünsch,Katrina McKinney,Oliver Smith,Tom Schaul,Timothy P. Lillicrap,Koray Kavukcuoglu,Demis Hassabis,Chris Apps,David Silver +41 more
TL;DR: The agent, AlphaStar, is evaluated, which uses a multi-agent reinforcement learning algorithm and has reached Grandmaster level, ranking among the top 0.2% of human players for the real-time strategy game StarCraft II.
Posted Content
Understanding deep learning requires rethinking generalization
TL;DR: The authors showed that deep neural networks can fit a random labeling of the training data, and that this phenomenon is qualitatively unaffected by explicit regularization, and occurs even if the true images are replaced by completely unstructured random noise.
Posted Content
Neural Message Passing for Quantum Chemistry
TL;DR: Using MPNNs, state of the art results on an important molecular property prediction benchmark are demonstrated and it is believed future work should focus on datasets with larger molecules or more accurate ground truth labels.
Posted Content
Relational inductive biases, deep learning, and graph networks
Peter W. Battaglia,Jessica B. Hamrick,Victor Bapst,Alvaro Sanchez-Gonzalez,Vinicius Zambaldi,Mateusz Malinowski,Andrea Tacchetti,David Raposo,Adam Santoro,Ryan Faulkner,Caglar Gulcehre,H. Francis Song,Andrew J. Ballard,Justin Gilmer,George E. Dahl,Ashish Vaswani,Kelsey R. Allen,Charlie Nash,Victoria Langston,Chris Dyer,Nicolas Heess,Daan Wierstra,Pushmeet Kohli,Matthew Botvinick,Oriol Vinyals,Yujia Li,Razvan Pascanu +26 more
TL;DR: It is argued that combinatorial generalization must be a top priority for AI to achieve human-like abilities, and that structured representations and computations are key to realizing this objective.