PPT-Do Deep Networks have Bad Local Minima?

Author : pamella-moone | Published Date : 2018-12-16

Brief survey on optimization landscape for neural networks Rong Ge Duke University Nonconvex optimization Theory NPhard Practice simple algorithmsSGD Difficulties

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Do Deep Networks have Bad Local Minima?: Transcript

Brief survey on optimization landscape for neural networks Rong Ge Duke University Nonconvex optimization Theory NPhard Practice simple algorithmsSGD Difficulties Saddle Points Highorder Saddles. 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. 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 . Deep Neural Networks . Huan Sun. Dept. of Computer Science, UCSB. March 12. th. , 2012. Major Area Examination. Committee. Prof. . Xifeng. . Yan. Prof. . Linda . Petzold. Prof. . Ambuj. Singh. 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. 1. Local Area Networks. Aloha. Slotted Aloha. CSMA (non-persistent, 1-persistent, . p-persistent). CSMA/CD. Ethernet. Token Ring. Networks: Local Area Networks. 2. Data Link. Layer. 802.3. Secada combs | bus-550. AI Superpowers: china, silicon valley, and the new world order. Kai Fu Lee. Author of AI Superpowers. Currently Chairman and CEO of . Sinovation. Ventures and President of . Sinovation. What’s new in ANNs in the last 5-10 years?. Deeper networks, . m. ore data, and faster training. Scalability and use of GPUs . ✔. Symbolic differentiation. ✔. reverse-mode automatic differentiation. 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 Jiang. Feb 17. Model formulation. . . . . . . …. Recall the model of fully-connected neural networks. . When . . Linear Networks. In the following slides, we only consider linear networks without bias:. 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. Topics: 1. st. lecture wrap-up, difficulty training deep networks,. image classification problem, using convolutions,. tricks to train deep networks . . Resources: http://www.cs.utah.edu/~rajeev/cs7960/notes/ . New-Generation Models & Methodology for Advancing Speech Technology. Li Deng . Microsoft Research, Redmond, USA. Keynote at . Odyssey Speaker/Language Recognition Workshop. Singapore, June. 26, 2012.

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