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.
Papers
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Proceedings Article
Synthesizing Programs for Images using Reinforced Adversarial Learning.
TL;DR: SPIRAL as discussed by the authors is an adversarially trained agent that generates a program which is executed by a graphics engine to interpret and sample images, trained with a distributed reinforcement learning setup without any supervision.
Posted Content
The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priors.
William H. Guss,Mario Ynocente Castro,Sam Devlin,Brandon Houghton,Noboru Sean Kuno,Crissman Loomis,Stephanie Milani,Sharada P. Mohanty,Keisuke Nakata,Ruslan Salakhutdinov,John Schulman,Shinya Shiroshita,Nicholay Topin,Avinash Ummadisingu,Oriol Vinyals +14 more
TL;DR: The MineRL Competition on Sample Efficient Reinforcement Learning using Human Priors is introduced, to foster the development of algorithms which can efficiently leverage human demonstrations to drastically reduce the number of samples needed to solve complex, hierarchical, and sparse environments.
Posted Content
Sample Efficient Adaptive Text-to-Speech
Yutian Chen,Yannis M. Assael,Brendan Shillingford,David Budden,Scott Reed,Heiga Zen,Quan Wang,Luis C. Cobo,Andrew Trask,Ben Laurie,Caglar Gulcehre,Aaron van den Oord,Oriol Vinyals,Nando de Freitas +13 more
TL;DR: Three strategies are introduced and benchmark three strategies at adapting the multi-speaker neural network to new speakers, obtaining state-of-the-art results in both sample naturalness and voice similarity with merely a few minutes of audio data from new speakers.
Proceedings ArticleDOI
Comparing multilayer perceptron to Deep Belief Network Tandem features for robust ASR
Oriol Vinyals,Suman V. Ravuri +1 more
TL;DR: It is suggested that DBNs outperform single layer MLPs under the clean condition, but the gains diminish as the noise level is increased, while using MFCCs in conjunction with the posteriors from DBN's outperforms merely using single DBNS in low to moderate noise conditions.
Journal Article
Speaker diarization : A review of recent research
Xavier Anguera,Simon Bozonnet,Nicholas Evans,Corinne Fredouille,Gerald Friedland,Oriol Vinyals +5 more
TL;DR: Speaker diarization is the task of determining "who spoke when" in an audio or video recording that contains an unknown amount of speech and also an unknown number of speakers as discussed by the authors.