Zobrazeno 1 - 10
of 11
pro vyhledávání: '"Cody Coleman"'
Autor:
Gu-Yeon Wei, Greg Diamos, Christine Cheng, David Kanter, David A. Patterson, Peter Mattson, Hanlin Tang, Vijay Janapa Reddi, Guenther Schmuelling, Cody Coleman, Paulius Micikevicius, Carole-Jean Wu
Publikováno v:
IEEE Micro. 40:8-16
In this article, we describe the design choices behind MLPerf, a machine learning performance benchmark that has become an industry standard. The first two rounds of the MLPerf Training benchmark helped drive improvements to software-stack performanc
Publikováno v:
ICPHM
A common scenario for many organizations in this information age entails volumes of data that are easily collected and stored in repositories, but are not subsequently leveraged to improve products or processes due to a lack of resources, such as kno
Autor:
Jian Zhang, Matei Zaharia, Cody Coleman, Daniel Kang, Christopher Ré, Kunle Olukotun, Luigi Nardi, Deepak Narayanan, Peter Bailis, Tian Zhao
Publikováno v:
ACM SIGOPS Operating Systems Review. 53:14-25
Researchers have proposed hardware, software, and algorithmic optimizations to improve the computational performance of deep learning. While some of these optimizations perform the same operations faster (e.g., increasing GPU clock speed), many other
The Engineer Research and Development Center, Information Technology Laboratory’s (ERDC-ITL’s) Big Data Analytics team specializes in the analysis of large-scale datasets with capabilities across four research areas that require vast amounts of d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2d14f8a3dc320cad04faea06de43a90b
https://doi.org/10.21079/11681/40203
https://doi.org/10.21079/11681/40203
Autor:
Cody Coleman, Edward Chou, Julian Katz-Samuels, Sean Culatana, Peter Bailis, Alexander C. Berg, Robert Nowak, Roshan Sumbaly, Matei Zaharia, I. Zeki Yalniz
Many active learning and search approaches are intractable for large-scale industrial settings with billions of unlabeled examples. Existing approaches search globally for the optimal examples to label, scaling linearly or even quadratically with the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::586c9dddd71ef4dcadc323f0ff5f8835
Autor:
Mark Charlebois, Dave Fick, Sachin Satish Idgunji, Yuchen Zhou, Michael Thomson, Ashish Sirasao, George Yuan, Anton Lokhmotov, Koichi Yamada, Tom St. John, Bing Yu, Jeff Jiao, Arun Tejusve Raghunath Rajan, Paulius Micikevicius, Ephrem C. Wu, Francisco Massa, Carole-Jean Wu, Hanlin Tang, David Lee, William Chou, Frank Wei, Jared Duke, Cody Coleman, Sam Davis, Jeffery Liao, Itay Hubara, Dilip Sequeira, Lingjie Xu, Pan Deng, Vijay Janapa Reddi, Guenther Schmuelling, Gennady Pekhimenko, Maximilien Breughe, Peng Meng, Greg Diamos, David Kanter, Colin Osborne, Thomas B. Jablin, Peizhao Zhang, Fei Sun, Pankaj Kanwar, Ramesh Chukka, J. Scott Gardner, Aaron Zhong, Christine Cheng, Peter Mattson, Brian M. Anderson
Publikováno v:
ISCA
Machine-learning (ML) hardware and software system demand is burgeoning. Driven by ML applications, the number of different ML inference systems has exploded. Over 100 organizations are building ML inference chips, and the systems that incorporate ex
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7a2417894de20d08d29ba667a4080f9a
Publikováno v:
Annual Conference of the PHM Society. 12:8
The manner in which a prognostics problem is framed is critical for enabling its solution by the proper method. Recently, data-driven prognostics techniques have demonstrated enormous potential when used alone, or as part of a hybrid solution in conj
Autor:
Yohsuke R. Miyamoto, Sergiy O. Nesterko, Justin Reich, Joseph Jay Williams, Jacob Whitehill, Cody Coleman
Publikováno v:
Journal of Learning Analytics; Vol 2 No 2 (2015): Learning Analytics and Learning Theory; 47-69
A long history of laboratory and field experiments has demonstrated that dividing study time into many sessions is often superior to massing study time into few sessions, a phenomenon widely known as the “spacing effect.” Massive open online cour
Publikováno v:
SSRN Electronic Journal.
High attrition rates in massive open online courses (MOOCs) have motivated growing interest in the automatic detection of student "stopout". Stopout classifiers can be used to orchestrate an intervention before students quit, and to survey students d
Autor:
Andrew D. Ho, Rebecca Petersen, Jacob Whitehill, Justin Reich, Cody Coleman, Curtis G. Northcutt, Glenn Lopez, John D. Hansen, Joseph Jay Williams, Isaac L. Chuang
Publikováno v:
SSRN Electronic Journal.
What happens when well-known universities offer online courses, assessments, and certificates of completion for free? Early descriptions of Massive Open Online Courses (MOOCs) have emphasized large enrollments, low certification rates, and highly edu