PPT-Do Deep Networks have Bad Local Minima?

Brief survey on optimization landscape for neural networks Rong Ge Duke University Nonconvex optimization Theory NPhard Practice simple algorithmsSGD Difficulties Saddle Points Highorder Saddles. Tutorial.. Session 2.. SIFT. Gonzalo . Vaca-Castano. Sift purpose. Find and describe interest points invariants to:. Scale. Rotation. Illumination. Viewpoint. Do it Yourself. Constructing a scale . space. Deep Learning @ . UvA. UVA Deep Learning COURSE - Efstratios Gavves & Max Welling. LEARNING WITH NEURAL NETWORKS . - . PAGE . 1. Machine Learning Paradigm for Neural Networks. The Backpropagation algorithm for learning with a neural network. ISHAY BE’ERY. ELAD KNOLL. OUTLINES. . Motivation. Model . c. ompression: mimicking large networks:. FITNETS : HINTS FOR THIN DEEP NETS . (A. Romero, 2014). DO DEEP NETS REALLY NEED TO BE DEEP . (Rich Caruana & Lei Jimmy Ba 2014). The End of the Joan of Arc . Teacher Recruitment Strategy. BEST-NC Innovation Lab. September 28, 2016. Cary, NC. Southern Regional Education Board. Andy Baxter, Vice President for Educator Effectiveness. Original Words by Samuel Trevor Francis (1834-1925). Music, chorus, and alternate words by Bob Kauflin.. © 2008 Integrity’s Praise! Music/Sovereign Grace Praise (BMI). Sovereign Grace Music, a division of Sovereign Grace Ministries.. Functions can be simple . . minimum. Or a bit more complicated… with no minima despite appearance (saddle point) . . . saddle point. With degenerate minima – a whole line instead of a point . Rajdeep. . Dasgupta. CIDER Community Workshop, CA. May 08, 2016. Volcanic degassing. hazards. long-term climate. Bio-essential elements. Origin of life. Mantle melting. Chemical differentiation. Properties of asthenosphere. Deep . Learning. James K . Baker, Bhiksha Raj. , Rita Singh. Opportunities in Machine Learning. Great . advances are being made in machine learning. Artificial Intelligence. Machine. Learning. After decades of intermittent progress, some applications are beginning to demonstrate human-level performance!. SIFT. Gonzalo . Vaca-Castano. Sift purpose. Find and describe interest points invariants to:. Scale. Rotation. Illumination. Viewpoint. Do it Yourself. Constructing a scale . space. LoG. . Approximation. . Jude Shavlik. Yuting. . Liu (TA). Deep Learning (DL). Deep Neural Networks arguably the most exciting current topic in all of CS. Huge industrial and academic impact. Great intellectual challenges. New-Generation Models & Methodology for Advancing . AI & SIP. Li Deng . Microsoft Research, Redmond, . USA. Tianjin University, July 2-5, 2013. (including joint work with colleagues at MSR, U of Toronto, etc.) . How It WorksA local government or utility issues bonds to private investors that are repaid over many years with revenues from the network Certicates of Participation work along similar principles Few New-Generation Models & Methodology for Advancing . Speech Technology . and Information Processing. Li Deng . Microsoft Research, Redmond, . USA. CCF, . Beijing. , July . 8. , 2013. (including joint work with colleagues at MSR, U of Toronto, etc.) . Management and Radio Performance Improvement. Faris B. Mismar and Brian L. Evans. faris.mismar@utexas.edu. and . bevans@ece.utexas.edu. . MOTIVATION. Self-Organizing Networks. Cellular network faults impact SINR and data rates.