PPT-Approximate Dynamic Programmin

g and aCGH denoising Charalampos Babis E Tsourakakis ctsourakmathcmuedu Machine Learning Seminar 10 th January 11 Machine Learning Lunch Seminar. SA INTEGE PROGRAMMIN WIT A FIXE NUMBE VARIABLES W LENSTRA JR Universiteit van Amsterdam i show tha th intege linea programmin proble wit a fixe numbe o variable i polynomiall solvable Th proo depend o method fro geometr o numbers Th integer linear pr Approximate Fusing Current (A) Time to fuse (seconds) More than adequate current carrying capacity for grounding or lightning arrest. Fusing currents calculated using well known and accepted formula: University of Washington. Adrian Sampson, . Hadi. Esmaelizadeh,. 1. Michael . Ringenburg. , . Reneé. St. Amant,. 2. . Luis . Ceze. , . Dan Grossman. , Mark . Oskin. , Karin Strauss,. 3. and Doug Burger. Xiaoming. Sun and David P. Woodruff. Chinese Academy of Sciences and IBM Research-. Almaden. Streaming Models. Long sequence of items appear one-by-one. numbers, points, edges, …. (usually) . adversarially. Hardware: Challenges and Opportunities. Author. : Bingsheng He. (Nanyang Technological University, Singapore) . Speaker. : . Jiong . He . (Nanyang Technological University, Singapore. ). 1. What is Approximate Hardware?. By Venkatesh Ganti, Mong Li Lee, and Raghu Ramakrishnan. CSE6339 – Data exploration. Raghavendra Madala. In this presentation…. Introduction. Icicles. Icicle Maintenance. Icicle-Based Estimators. Davide Mottin, Alice . Marascu. , . Senjuti. . Basu. Roy. Gautam. Das, Themis . Palpanas. , . Yannis. . Velegrakis. Talk by Davide Mottin at Yahoo! Research Barcelona. Who am I?. Born. in Marostica. Andrew B. Kahng, . Seokhyeong Kang . VLSI CAD LABORATORY, . UC. San Diego. 49. th. Design Automation Conference. June 6. th. , 2012. Outline. Background and Motivation. Accuracy Configurable Adder Design. Programming using Halfspace Queries and . Multiscale. . Monge. Decomposition. . Charalampos (Babis) E. Tsourakakis. ctsourak@math.cmu.edu. SODA 2011. 25. Ulya. . R. . Karpuzcu. ukarpuzc@umn.edu. . 12/01/2015. Outline. Background. Pitfalls & Fallacies. Practical Guidelines. 2. 12/01/2015. On Quantification of Accuracy Loss in Approximate Computing. University of Washington. Adrian Sampson, . Hadi. Esmaelizadeh,. 1. Michael . Ringenburg. , . Reneé. St. Amant,. 2. . Luis . Ceze. , . Dan Grossman. , Mark . Oskin. , Karin Strauss,. 3. and Doug Burger. Computation Circuits. Wei-Ting Jonas Chan. 1. , Andrew B. Kahng. 1. , . Seokhyeong Kang. 1. , . Rakesh. Kumar. 2. , and John Sartori. 3. 1. VLSI . CAD LABORATORY, . UC San Diego. 2. PASSAT GROUP, Univ. of Illinois. Matagorda County, TX. CE 394K Fall 2017. Sydney Kase. Essential Questions. Why does FEMA create the . NFHL. and . FIRM. maps?. Why does it take so long to produce effective floodplain maps?. What does the . dynamic data structures. Shachar. Lovett. IAS. Ely . Porat. Bar-. Ilan. University. Synergies in lower bounds, June 2011. Information theoretic lower bounds. Information theory. is a powerful tool to prove lower bounds, e.g. in data structures.