PPT-Learning With Dynamic Group Sparsity

Junzhou Huang Xiaolei Huang Dimitris Metaxas Rutgers University Lehigh University Rutgers University Outline Problem Applications where the useful information is very less compared with the given data . eb pages can made more liv ely dynamic or in teractiv DHTML tec hniques With DHTML ou can prescrib actions triggered bro wser ev en ts to mak the page more liv ely and resp onsiv e Suc actions ma alter the con ten and app earance of an parts of the the . nature and . microfoundations. of (sustainable) enterprise performance. David J. . Teece. Shinyoung. Kim. Operations Management. What. are. Dynamic Capabilities?. Dynamic Capabilities. = Dynamic + Capabilities . Sung . Ju. Hwang. 1. , . Fei. Sha. 2. and Kristen Grauman. 1. 1 University . of Texas at . Austin, 2 University of Southern California. Problem. Sharing features between sub/. superclasses. A single visual instance can have multiple labels at different levels of semantic granularity... Manufacturing . Systems. By . Djamila. . Ouelhadj. and . Sanja. . Petrovic. Okan Dükkancı. 02.12.2013. Introduction. Dynamic . environments . with inevitable unpredictable real time events. ;. Machine failures . Sparsity. and Geometry Constrained Dictionary Learning for Action. Recognition from Depth Maps. Jiajia. . Luo. , Wei Wang, and . Hairong. Qi. The University of Tennessee, Knoxville. Presented by: Marwan . systems. STREAM-Engineering Doctorate project. By: Biniam . Biruk. Ashagre. Academic Supervisors: Dr Guangtao Fu. Prof David Butler. Industrial Supervisor: Ms Kerry Davidson. Safe and Sure project weekly meeting: 29/08/2013 . Kibble-. Zurek. Michael Kolodrubetz. Boston University. In collaboration with: . B.K. Clark, D. . Huse. (Princeton). A. . Polkovnikov. , A. Katz (BU). Kibble-. Zurek. Scaling. Disordered. Ordered. Kibble-. *. Dynamic logic is temporary (. transient. ) in that output levels will remain valid only for a certain period of time. Static logic retains its output level as long as power is applied. Dynamic logic is normally done with charging and selectively discharging capacitance (i.e. capacitive circuit nodes). Sabareesh Ganapathy. Manav Garg. Prasanna. . Venkatesh. Srinivasan. Convolutional Neural Network. State of the art in Image classification. Terminology – Feature Maps, Weights. Layers - Convolution, . Ron Rubinstein. Advisor: Prof. Michael . Elad. October 2010. Signal Models. Signal models. . are a fundamental tool for solving low-level signal processing tasks. Noise Removal. Image Scaling. Compression. sparse acoustic modeling for speech separation. Afsaneh . Asaei. Joint work with: . Mohammad . Golbabaee. ,. Herve. Bourlard, . Volkan. . Cevher. φ. 21. φ. 52. s. 1. s. 2. s. 3. . s. 4. s. 5. x. Sung . Ju. Hwang. 1. , . Fei. Sha. 2. and Kristen Grauman. 1. 1 University . of Texas at . Austin, 2 University of Southern California. Problem. Experimental results. Conclusion/Future Work. . How Unthinkable?. Army Operational Knowledge Management. 21 October 2009. Dr. Mark E. Nissen . Center for Edge Power. US Naval Postgraduate School. http://www.nps.edu/Academics/Centers/CEP/. 2. KM Background. SCNN: An Accelerator for Compressed-sparse Convolutional Neural Networks. 9 authors @ NVIDIA, MIT, Berkeley, Stanford. ISCA . 2017. Convolution operation. Reuse. Memory: size vs. access energy. Dataflow decides reuse.