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Seeing LHC/SPS beam dumps with Deep Convolutional Neural Networks ( ConvNets )

Seeing LHC/SPS beam dumps with Deep Convolutional Neural Networks ( ConvNets )

Seeing LHC/SPS beam dumps with Deep Convolutional Neural Networks ( ConvNets ). Bren, Francesco, Elli. Overview. Computer Vision and ConvNets Digital images and classification problems How computers see with deep Convolutional Neural Networks: deep learning

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628 views • 62 slides
Molecule Construction and Design Using Vector NTI 10 Advance

Molecule Construction and Design Using Vector NTI 10 Advance

Molecule Construction and Design Using Vector NTI 10 Advance. Yi-Bu Chen, Ph.D. Bioinformatics Specialist Norris Medical Library University of Southern California 323-442-3309 yibuchen@belen.hsc.usc.edu. Workshop Outline. Overview of molecule types and creation methods

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1.11k views • 61 slides
Optimization with Neural Networks

Optimization with Neural Networks

Optimization with Neural Networks. Presented by: Nasim Zeynolabedini shoale Hashemi Zahra Rashti Instructor: Dr. S. Bagheri Sharif University of Technology Ordibehesht 1384. Introduction. Optimization Problem: A Problem with a cost function that is to be minimized or maximized

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1.36k views • 59 slides
Learning to Diversify using implicit feedback

Learning to Diversify using implicit feedback

Learning to Diversify using implicit feedback. Karthik Raman , Pannaga Shivaswamy & Thorsten Joachims Cornell University. News Recommendation. U.S. Economy. Soccer. Tech Gadgets. News Recommendation. Relevance-Based?. Becomes too redundant, ignoring some interests of the user.

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469 views • 31 slides
Masquerader detection using SVM with String Kernel

Masquerader detection using SVM with String Kernel

Masquerader detection using SVM with String Kernel. Statistical Learning Theory Special Issue in AI 2003 fall December 4, 2003 Jeongseok Seo. Contents. Introduction Related works SVM and String kernel Experiment and Results Future works References. Introduction (1/2). Masquerader.

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470 views • 29 slides
Principal Component Analysis

Principal Component Analysis

Principal Component Analysis. CMPUT 466/551 Nilanjan Ray. Overview. Principal component analysis (PCA) is a way to reduce data dimensionality PCA projects high dimensional data to a lower dimension

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262 views • 22 slides
klewtv /news/local/WSU-robots-253558541.html?tab= video&c =y

klewtv /news/local/WSU-robots-253558541.html?tab= video&c =y

http:// www.klewtv.com /news/local/WSU-robots-253558541.html?tab= video&c =y. Final Project Proposals Exercise 4. Sub-MDPs re-usable in new tasks Can save time Know when to re-use? What if you have too many? SMDP Options: policy termination, init. set

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121 views • 3 slides

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