Machine learning at the limit

Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015

Volumn , Issue , 2015, Pages 233-242

  Canny, John ^a,b   Zhao, Huasha ^a   Jaros, Bobby ^b   Chen, Ye ^c   Mao, Jiangchang ^d  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b YAHOO RESEARCH   (United States)

^c Microsoft ^*  (China)

^d MICROSOFT   (United States)

Author keywords

Big Data; Distributed Systems; Scalable Machine Learning

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER ARCHITECTURE; DESIGN; DIGITAL STORAGE; LEARNING ALGORITHMS; LEARNING SYSTEMS; MATRIX ALGEBRA; METADATA; SCHEDULING ALGORITHMS;

DISTRIBUTED MACHINE LEARNING; DISTRIBUTED SYSTEMS; END-TO-END DESIGN; LARGE-SCALE MACHINE LEARNING; PERFORMANCE LEVEL; PRACTICAL NETWORKS; REAL-WORLD PROBLEM; SCALABLE MACHINE LEARNING;

BIG DATA;

EID: 84963749963     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/BigData.2015.7363760     Document Type: Conference Paper

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