PPT-Provable Learning of Noisy-OR Networks

Rong Ge Duke University Joint work with Sanjeev Arora Tengyu Ma Andrej Risteski Provable Learning of NoisyOR Networks STOC 2017 arxiv161208795 New practical algorithms for learning NoisyOR networks via symmetric NMF. cmuedu Huan Xu Dept of Mech Engineering National Univ of Singapore Singapore 117576 mpexuhnusedusg Chenlei Leng Department of Statistics University of Warwick Coventry CV4 7AL UK CLengwarwickacuk Abstract Sparse Subspace Clustering SSC and LowRank Re Models, Adversaries, Reductions. Cryptography / Cryptology. “from.  . Greek.  . κρυ. πτός.  . kryptós. , "hidden, secret"; and . γράφειν. . graphein. , "writing", or . -λογία. 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. Chun Lam Chan. , Pak . Hou. . Che. and . Sidharth. . Jaggi. The Chinese University of Hong Kong. Venkatesh. . Saligrama. Boston University. Non-adaptive probabilistic group testing with noisy measurements: Near-optimal bounds with efficient algorithms. 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. . Defaults. Foundations of AI. Given an N X N matrix, and given N colors, color the matrix in such a way that:. -all cells are colored;. - each color occurs exactly once in each row;. - each color occurs exactly once in each column;. Benjamin . Fuller. , Leonid . Reyzin. , and Adam Smith. Additional Work with . Ran Canetti, . Xianrui. . Meng. , Omer . Paneth. August 26, 2014. Outline. Noisy Authentication Sources. Fuzzy Extractors. 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. Brian Aronson. Review of ego networks. Ego network (personal network). Ego: Focal node/respondent. Alter: Actors ego has ties with. Dyad: Pair of individuals. Ties. (Ego). D. C. B. Tie types. Friends. Ali Cole. Charly. . Mccown. Madison . Kutchey. Xavier . henes. Definition. A directed network based on the structure of connections within an organism's brain. Many inputs and only a couple outputs. ). Prof. . Ralucca Gera, . Applied Mathematics Dept.. Naval Postgraduate School. Monterey, California. rgera@nps.edu. Excellence Through Knowledge. Learning Outcomes. I. dentify . network models and explain their structures. 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. 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:. The Spelling Correction Task. Applications for spelling correction. 2. Web search. Phones. Word processing. Spelling Tasks. Spelling Error Detection. Spelling Error Correction:. Autocorrect . hte. .