PPT-Non-adaptive probabilistic group testing with noisy measure

Chun Lam Chan Pak Hou Che and Sidharth Jaggi The Chinese University of Hong Kong Venkatesh Saligrama Boston University Nonadaptive probabilistic group testing with noisy measurements Nearoptimal bounds with efficient algorithms. Qiming. Zhu. Supervisor: Prof. John . Soraghan. Centre for excellence in Signal and Image Processing. Dept Electronic and Electrical Engineering. q.zhu@strath.ac.uk. Introduction. Speech Enhancement. Review Summarizing . Topic. . The topic is the subject that the selection is about. To find the topic of a selection, ask the simple question, . “. Who or what is the . selection about. ?. ”. . Shou-pon. Lin. Advisor: Nicholas F. . Maxemchuk. Department. . of. . Electrical. . Engineering,. . Columbia. . University,. . New. . York,. . NY. . 10027. . Problem: . Markov decision process or Markov chain with exceedingly large state space. A science story. Darin J. . Ulness. Department of Chemistry. Concordia College, Moorhead, MN. Spectroscopy. Using . light. to gain information about . matter. Spectra. Transition frequencies. Time dynamics. Yin “David” Yang .  . Zhenjie. Zhang. .  . Gerome . Miklau. . Prev. . Session: Marianne . Winslett. .  . Xiaokui Xiao. 1. What we talked in the last session. Privacy is a major concern in data publishing. . Papailiopoulos. Convergence Rates of . Hogwild. !. Today. Single Machine, Multi-core. Today. Asynchronous. Processor 1. Processor 2. Processor P. SGD on sparse functions. Def. : . . . Hyperedge. Chapter 1: An Overview of Probabilistic Data Management. 2. Objectives. In this chapter, you will:. Get to know what uncertain data look like. Explore causes of uncertain data in different applications. LP decoding for non-linear (disjunctive) measurements. Chun Lam Chan, . Sidharth. . Jaggi. and Samar . Agnihotri. The Chinese University of Hong Kong. Venkatesh. . Saligrama. Boston University. 2. . Rob Fergus (New York University). Yair Weiss (Hebrew University). Antonio Torralba (MIT). . Presented by Gunnar Atli Sigurdsson. TexPoint fonts used in EMF. . Read the TexPoint manual before you delete this box.: AAAAAAAAAA. Rong Ge. Duke University. Joint work with Sanjeev Arora, . Tengyu. Ma, Andrej . Risteski. “Provable Learning of Noisy-OR Networks” . STOC 2017. arxiv:1612.08795. “New practical algorithms for learning Noisy-OR networks via symmetric NMF”. Spikes in trigger rate. Periodic:. With B ON in 2008 . Without B on during MWGR18 . Sporadic . MWGR 19. Strip noise profile. 6 may . 22 April. REASON: HV problem in RB1 out sect 12. Noisy topology. Chapter 7: Probabilistic Query Answering (5). 2. Objectives. In this chapter, you will:. Explore the definitions of more probabilistic query types. Probabilistic skyline query. Probabilistic reverse skyline query. CS772A: Probabilistic Machine Learning. Piyush Rai. Course Logistics. Course Name: Probabilistic Machine Learning – . CS772A. 2 classes each week. Mon/. Thur. 18:00-19:30. Venue: KD-101. All material (readings etc) will be posted on course webpage (internal access). Features of adaptive immunity:. Adaptive immunity is characterized by the following:. 1. Antigenic specificity: (. highly specific).. 2. Diversity : . ( each antigen there is specific T-cell and B-cell for it)..