PPT-SPOOF: Sum-Product Optimization and Operator Fusion for Large-Scale Machine Learning

Author : luanne-stotts | Published Date : 2018-10-26

Tarek Elgamal 2 Shangyu Luo 3 Matthias Boehm 1 Alexandre V Evfimievski 1 Shirish Tatikonda 4 Berthold Reinwald 1 Prithviraj Sen 1 1 IBM Research

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SPOOF: Sum-Product Optimization and Operator Fusion for Large-Scale Machine Learning: Transcript

Tarek Elgamal 2 Shangyu Luo 3 Matthias Boehm 1 Alexandre V Evfimievski 1 Shirish Tatikonda 4 Berthold Reinwald 1 Prithviraj Sen 1 1 IBM Research . to Speech . EE 225D - . Audio Signal Processing in Humans and Machines. Oriol Vinyals. UC Berkeley. This is my biased view about deep learning and, more generally, machine learning past and current research!. Pritam. . Sukumar. & Daphne Tsatsoulis. CS 546: Machine Learning for Natural Language Processing. 1. What is Optimization?. Find the minimum or maximum of an objective function given a set of constraints:. Felix Alamo. Career Portals Ms.Estep. 7. th. Grade 7. th. Period. 10/15-10/17. Printing Machine Operator. Printing Machine Operators control the printing machines and/or fix them.. To be a Printing Machine Operator you need 1 to 3 years of college.. Jimmy Lin and Alek . Kolcz. Twitter, Inc.. Presented by: Yishuang Geng and Kexin Liu. 2. Outline. •Is twitter big data? . •How . can machine learning help twitter?. •Existing challenges?. •Existing literature of large-scale learning. Machine Learning. Large scale machine learning. Machine learning and data. Classify between confusable words.. E.g., {to, two, too}, {then, than}.. For breakfast I ate _____ eggs.. “It’s not who has the best algorithm that wins. . via Brain simulations . Andrew . Ng. Stanford University. Adam Coates Quoc Le Honglak Lee Andrew Saxe Andrew Maas Chris Manning Jiquan Ngiam Richard Socher Will Zou . Thanks to:. Tarek Elgamal. 2. , . Shangyu. Luo. 3. , . Matthias Boehm. 1. , Alexandre V. Evfimievski. 1. , . Shirish. Tatikonda. 4. , . Berthold Reinwald. 1. , . Prithviraj. Sen. 1. 1. IBM Research – . By Namita Dave. Overview. What are compiler optimizations?. Challenges with optimizations. Current Solutions. Machine learning techniques. Structure of Adaptive compilers. Introduction. O. ptimization . Jianfu Chen. Computer Science Department. Stony Brook University. Machine learning learns an idealized model of the . real . world.. . . . . 1 + 1 = 2. . . ?. Prod1 -> class1. Madan Musuvathi. . Visiting Professor, UCLA . Principal Researcher, Microsoft Research. Course Project. Write-ups due June 1. st. Project presentations . 12 presentations, 10 mins each, 15 min slack. Bahrudin Hrnjica, MVP. Agenda. Intro to ML. Types of ML. dotNET and ML-tools and libraries. Demo01: ANN with C#. Demo02: GP with C#. .NET Tools – Acord.NET, GPdotNET. Summary. Machine Learning?. method of teaching computers to make predictions based on data.. OO. L 2. 0. 12 KY. O. T. O. Briefing & Report. By: Masayuki . Kouno. . (D1) & . Kourosh. . Meshgi. . (D1). Kyoto University, Graduate School of Informatics, Department of Systems Science. Ishii Lab (Integrated System Biology). OO. L 2. 0. 12 KY. O. T. O. Briefing & Report. By: Masayuki . Kouno. . (D1) & . Kourosh. . Meshgi. . (D1). Kyoto University, Graduate School of Informatics, Department of Systems Science. Ishii Lab (Integrated System Biology). Institute of High Energy Physics, CAS. Wang Lu (Lu.Wang@ihep.ac.cn). Agenda. Introduction. Challenges and requirements of anomaly detection in large scale storage systems . Definition and category of anomaly.

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