Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Bittorf, Victor"'
Autor:
Karargyris, Alexandros, Umeton, Renato, Sheller, Micah J., Aristizabal, Alejandro, George, Johnu, Bala, Srini, Beutel, Daniel J., Bittorf, Victor, Chaudhari, Akshay, Chowdhury, Alexander, Coleman, Cody, Desinghu, Bala, Diamos, Gregory, Dutta, Debo, Feddema, Diane, Fursin, Grigori, Guo, Junyi, Huang, Xinyuan, Kanter, David, Kashyap, Satyananda, Lane, Nicholas, Mallick, Indranil, Mascagni, Pietro, Mehta, Virendra, Natarajan, Vivek, Nikolov, Nikola, Padoy, Nicolas, Pekhimenko, Gennady, Reddi, Vijay Janapa, Reina, G Anthony, Ribalta, Pablo, Rosenthal, Jacob, Singh, Abhishek, Thiagarajan, Jayaraman J., Wuest, Anna, Xenochristou, Maria, Xu, Daguang, Yadav, Poonam, Rosenthal, Michael, Loda, Massimo, Johnson, Jason M., Mattson, Peter
Medical AI has tremendous potential to advance healthcare by supporting the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving provider and patient experience. We argue that unlocking this potential re
Externí odkaz:
http://arxiv.org/abs/2110.01406
Autor:
Mattson, Peter, Cheng, Christine, Coleman, Cody, Diamos, Greg, Micikevicius, Paulius, Patterson, David, Tang, Hanlin, Wei, Gu-Yeon, Bailis, Peter, Bittorf, Victor, Brooks, David, Chen, Dehao, Dutta, Debojyoti, Gupta, Udit, Hazelwood, Kim, Hock, Andrew, Huang, Xinyuan, Ike, Atsushi, Jia, Bill, Kang, Daniel, Kanter, David, Kumar, Naveen, Liao, Jeffery, Ma, Guokai, Narayanan, Deepak, Oguntebi, Tayo, Pekhimenko, Gennady, Pentecost, Lillian, Reddi, Vijay Janapa, Robie, Taylor, John, Tom St., Tabaru, Tsuguchika, Wu, Carole-Jean, Xu, Lingjie, Yamazaki, Masafumi, Young, Cliff, Zaharia, Matei
Machine learning (ML) needs industry-standard performance benchmarks to support design and competitive evaluation of the many emerging software and hardware solutions for ML. But ML training presents three unique benchmarking challenges absent from o
Externí odkaz:
http://arxiv.org/abs/1910.01500
Autor:
Sridhar, Srikrishna, Bittorf, Victor, Liu, Ji, Zhang, Ce, Ré, Christopher, Wright, Stephen J.
Many problems in machine learning can be solved by rounding the solution of an appropriate linear program (LP). This paper shows that we can recover solutions of comparable quality by rounding an approximate LP solution instead of the ex- act one. Th
Externí odkaz:
http://arxiv.org/abs/1311.2661
We describe an asynchronous parallel stochastic coordinate descent algorithm for minimizing smooth unconstrained or separably constrained functions. The method achieves a linear convergence rate on functions that satisfy an essential strong convexity
Externí odkaz:
http://arxiv.org/abs/1311.1873
This paper describes a new approach, based on linear programming, for computing nonnegative matrix factorizations (NMFs). The key idea is a data-driven model for the factorization where the most salient features in the data are used to express the re
Externí odkaz:
http://arxiv.org/abs/1206.1270
Akademický článek
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Autor:
Kornacker, Marcel, Behm, Alexander, Bittorf, Victor, Bobrovytsky, Taras, Ching, Casey, Choi, Alan, Erickson, Justin, Grund, Martin, Hecht, Daniel, Jacobs, Matthew, Joshi, Ishaan, Kuff, Lenni, Kumar, Dileep, Leblang, Alex, Li, Nong, Pandis, Ippokratis, Robinson, Henry, Rorke, David, Rus, Silvius, Russel, John
Publikováno v:
Big Data (9783658115883); 2016, p159-178, 20p
Akademický článek
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Autor:
Ji Liu1 JI.LIU.UWISC@GMAIL.COM, Wright, Stephen J.1, Ré, Christopher2 CHRISMRE@CS.STANFORD.EDU, Bittorf, Victor1 BITTORF@CS.WISC.EDU, Sridhar, Srikrishna1 SRIKRIS@CS.WISC.EDU
Publikováno v:
Journal of Machine Learning Research. 2015, Vol. 16, p285-322. 38p.