PPT-Machine Learning in Compiler Optimization

Author : myesha-ticknor | Published Date : 2017-07-25

By Namita Dave Overview What are compiler optimizations Challenges with optimizations Current Solutions Machine learning techniques Structure of Adaptive compilers

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Machine Learning in Compiler Optimization: Transcript

By Namita Dave Overview What are compiler optimizations Challenges with optimizations Current Solutions Machine learning techniques Structure of Adaptive compilers Introduction O ptimization . 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:. g and . aCGH. denoising. . Charalampos (Babis) E. Tsourakakis. ctsourak@math.cmu.edu. . Machine Learning Seminar. . . 10. th. January ‘11. Machine Learning Lunch Seminar. Compilers. Basic compiler Functions . (1). A. . high-level. . programming. . language. . is usually. . described. . in. . terms. . of. . a. . grammar.. This. . grammar. . specifies. . the. http://hunch.net/~mltf. John Langford. Microsoft Research. Machine Learning in the present. Get a large amount of labeled data . . where . . Learn a predictor . Use the predictor.. The Foundation: Samples + Representation + Optimization. Or, How much wood could a woodchuck chuck if a woodchuck could chuck . n. pieces of wood in parallel?. Wojtek Rajski, Nels Oscar, David Burri, Alex Diede. Introduction. We have seen how to improve performance through exploitation of:. Compilers. Basic compiler Functions . (1). A. . high-level. . programming. . language. . is usually. . described. . in. . terms. . of. . a. . grammar.. This. . grammar. . specifies. . the. Jianfu Chen. Computer Science Department. Stony Brook University. Machine learning learns an idealized model of the . real . world.. . . . . 1 + 1 = 2. . . ?. Prod1 -> class1. Kai Liu. Purdue University. 1. Andrés Tovar. Indiana Univ. - Purdue Univ. Indianapolis. Emily NutWell. Honda R&D Americas. Duane Detwiler. Honda R&D Americas. Systematic Design Optimization Approach . Prof. Gennady . Pekhimenko. University of Toronto. Winter 2018. The content of this lecture is adapted from the lectures of . Todd Mowry and Phillip Gibbons. CSC D70: . Compiler Optimization. Introduction, Logistics. 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.. T. Chen, T. Moreau, Z. Jiang, L. Zheng, S. Jiao, E. Yan, H. Shen, M. Cowan, L. Wang, Y. Hu, L. . Ceze. , C. . Guestrin. , and A. Krishnamurthy . Presentation by Grzegorz . 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). on a small trapped-ion quantum computer. Norbert M. . Linke. Joint Quantum Institute, University of Maryland, College Park, MD USA. 4 Feb 2019, UT Quantum Workshop. College Park, Maryland, USA. Overview.

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