J
Jeffrey Dean
Researcher at Google
Publications - 255
Citations - 207859
Jeffrey Dean is an academic researcher from Google. The author has contributed to research in topics: Deep learning & Web search query. The author has an hindex of 83, co-authored 242 publications receiving 179031 citations. Previous affiliations of Jeffrey Dean include University of Washington & World Health Organization.
Papers
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Journal ArticleDOI
The Beckman report on database research
Daniel J. Abadi,Rakesh Agrawal,Anastasia Ailamaki,Magdalena Balazinska,Philip A. Bernstein,Michael J. Carey,Surajit Chaudhuri,Jeffrey Dean,AnHai Doan,Michael J. Franklin,Johannes Gehrke,Laura M. Haas,Alon Halevy,Joseph M. Hellerstein,Yannis Ioannidis,H. V. Jagadish,Donald Kossmann,Samuel Madden,Sharad Mehrotra,Tova Milo,Jeffrey F. Naughton,Raghu Ramakrishnan,Volker Markl,Christopher Olston,Beng Chin Ooi,Christopher Ré,Dan Suciu,Michael Stonebraker,Todd Walter,Jennifer Widom +29 more
TL;DR: To make the most of the enormous opportunities at hand will require focusing on five research areas, according to database researchers, who paint big data as a defining challenge.
Patent
Methods and apparatus for ranking documents
TL;DR: In this article, methods and apparatus are described for scoring documents in response, in part, to parameters related to the document, source, and/or cluster score, and a cluster score corresponds with at least one document within the cluster.
Proceedings ArticleDOI
Dynamic control flow in large-scale machine learning
Yuan Yu,Martín Abadi,Paul Barham,Eugene Brevdo,Michael Burrows,Andy Davis,Jeffrey Dean,Sanjay Ghemawat,Tim Harley,Peter Hawkins,Michael Isard,Manjunath Kudlur,Rajat Monga,Derek G. Murray,Xiaoqiang Zheng +14 more
TL;DR: This paper describes the design of the programming model, and its implementation in TensorFlow, a distributed machine learning system, and describes the use of dataflow graphs to represent machine learning models, offering several distinctive features.
Journal ArticleDOI
Microscope 2.0: An Augmented Reality Microscope with Real-time Artificial Intelligence Integration.
Po-Hsuan Cameron Chen,Krishna Gadepalli,Robert C. MacDonald,Yun Liu,Kunal Nagpal,Timo Kohlberger,Jeffrey Dean,Greg S. Corrado,Jason D. Hipp,Martin C. Stumpe +9 more
TL;DR: The Augmented Reality Microscope (ARM) as mentioned in this paper is a cost-effective solution to the integration of AI, which overlays AI-based information onto the current view of the sample through the optical pathway in real-time, enabling seamless integration of the AI into the regular microscopy workflow.
Posted ContentDOI
The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink
David A. Patterson,Joseph E. Gonzalez,Urs Palo Alto Holzle,Quoc V. Le,Chen Liang,Lluís-Miquel Munguía,Daniel Rothchild,David R. So,Maud Texier,Jeffrey Dean +9 more
TL;DR: In this article , the authors show four best practices to reduce ML training energy and carbon dioxide emissions, and they predict that by 2030, total carbon emissions from training will decline significantly.