PPT-Frank-Wolfe optimization insights in machine learning

Author : jane-oiler | Published Date : 2016-10-31

Simon LacosteJulien INRIA École Normale Supérieure SIERRA Project Team SMILE November 4 th 2013 Outline FrankWolfe optimization FrankWolfe for structured

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Frank-Wolfe optimization insights in machine learning: Transcript

Simon LacosteJulien INRIA École Normale Supérieure SIERRA Project Team SMILE November 4 th 2013 Outline FrankWolfe optimization FrankWolfe for structured prediction links with previous algorithms. 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. 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. 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. 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 . First order methods For convex optimization J. Saketha Nath (IIT Bombay; Microsoft) Topics Part – I Optimal methods for unconstrained convex programs Smooth objective Non-smooth objective Part – II 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). th World Machine Learni ng and Deep Learning Congress and World Congress on Computer Science, Machine Learning and Big Data on August 30 - 31, 2018 Dubai, UAE Extended Abstract Vol. 1, Iss. 3 2018 J V. . Kain. , M. Fraser, B. Goddard, S. . Hirlander. , M. Schenk, F. . Velotti. CERN, EPFL, University of Malta. Lots of input from S. Levine’s lectures on Deep Reinforcement Learning at UC Berkeley . 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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