PPT-Neural Network Approximation of High-dimensional Functions

Author : lindy-dunigan | Published Date : 2016-11-17

Peter Andras School of Computing and Mathematics Keele University pandraskeeleacuk Overview Highdimensional functions and lowdimensional manifolds Manifold mapping

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Neural Network Approximation of High-dimensional Functions: Transcript

Peter Andras School of Computing and Mathematics Keele University pandraskeeleacuk Overview Highdimensional functions and lowdimensional manifolds Manifold mapping Function approximation over lowdimensional projections. Prasad . Raghavendra. . Ning. Chen C. . . Thach. . Nguyen . . . Atri. . Rudra. . . Gyanit. Singh. University of Washington. Roee . Engelberg. Technion. University. A Mini-Survey. Chandra . Chekuri. Univ. of Illinois, Urbana-Champaign. Submodular Set Functions. A function . f. : 2. N. . . . R . is submodular if. . f(A. ) + . f(B. ) ≥ . f(A. . B. ) + . Algorithms. and Networks 2014/2015. Hans L. . Bodlaender. Johan M. M. van Rooij. C-approximation. Optimization problem: output has a value that we want to . maximize . or . minimize. An algorithm A is an . Algorithms. and Networks 2015/2016. Hans L. . Bodlaender. Johan M. M. van Rooij. TexPoint fonts used in EMF. . Read the TexPoint manual before you delete this box.: . A. A. A. A. A. A. A. A. A. A. What to do if a problem is. A Mini-Survey. Chandra . Chekuri. Univ. of Illinois, Urbana-Champaign. Submodular Set Functions. A function . f. : 2. N. . . . R . is submodular if. . f(A. ) + . f(B. ) ≥ . f(A. . B. ) + . What are Artificial Neural Networks (ANN)?. ". Colored. neural network" by Glosser.ca - Own work, Derivative of File:Artificial neural . network.svg. . Licensed under CC BY-SA 3.0 via Commons - https://commons.wikimedia.org/wiki/File:Colored_neural_network.svg#/media/File:Colored_neural_network.svg. δ. -Timeliness. Carole . Delporte-Gallet. , . LIAFA . UMR 7089. , Paris VII. Stéphane Devismes. , VERIMAG UMR 5104, Grenoble I. Hugues Fauconnier. , . LIAFA . UMR 7089. , Paris VII. LIAFA. Motivation. Grigory. . Yaroslavtsev. . Penn State + AT&T Labs - Research (intern). Joint work with . Berman (PSU). , . Bhattacharyya (MIT). , . Makarychev. (IBM). , . Raskhodnikova. (PSU). Directed. Spanner Problem. How accurate is your estimate?. Differential Notation. The Linear Approximation to . y. = . f. (. x. ) is often written using the “differentials” . dx. and . dy. . In this notation, . dx. is used instead of . When the best just isn’t possible. Jeff Chastine. Approximation Algorithms. Some NP-Complete problems are too important to ignore. Approaches:. If input small, run it anyway. Consider special cases that may run in polynomial time. of . submodular. and XOS . functions by juntas. Vitaly. Feldman and Jan . Vondrâk. IBM . Research - . Almaden. Prelims:. Submodular. and XOS. Functions; . Learning. $20. $27. Approximation by juntas. Dr. Abdul Basit. Lecture No. 1. Course . Contents. Introduction and Review. Learning Processes. Single & Multi-layer . Perceptrons. Radial Basis Function Networks. Support Vector and Committee Machines. on SU(2) Group Manifold . and N=4 Gauged Supergravity. . . Patharadanai Nuchino. . Dr. Parinya Karndumri. June 8, 2016 . Room Anek, Baansuan-Khunta and Golf Resort Hotel, Ubon Ratchathani, Thailand. A way of converting between units for problem solving. Remember all units have to be in meters, kilograms, and seconds. You can also use dimensional analysis as a way of checking your units to make sure your problem is correct.

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