S
Scott Reed
Researcher at Google
Publications - 57
Citations - 85613
Scott Reed 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 33, co-authored 56 publications receiving 63000 citations. Previous affiliations of Scott Reed include University of Michigan.
Papers
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Proceedings Article
Parallel Multiscale Autoregressive Density Estimation.
Proceedings Article
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: In this article, a meta-learning approach for adaptive text-to-speech (TTS) with few data is presented, where the aim is to produce a network that requires few data at deployment time to rapidly adapt to new speakers.
Posted Content
Neural Arithmetic Logic Units
TL;DR: Experiments show that NALU-enhanced neural networks can learn to track time, perform arithmetic over images of numbers, translate numerical language into real-valued scalars, execute computer code, and count objects in images.
Posted Content
Scaling data-driven robotics with reward sketching and batch reinforcement learning
Serkan Cabi,Sergio Gomez Colmenarejo,Alexander Novikov,Ksenia Konyushkova,Scott Reed,Rae Jeong,Konrad Zolna,Yusuf Aytar,David Budden,Mel Vecerik,Oleg P. Sushkov,David J. Barker,Jonathan Scholz,Misha Denil,Nando de Freitas,Ziyu Wang +15 more
TL;DR: In this paper, the authors present a framework for data-driven robotics that makes use of a large dataset of recorded robot experience and scales to several tasks using learned reward functions, and apply this framework to accomplish three different object manipulation tasks on a real robot platform.
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.