PDF-Tensor decompositions for learning latest variable models

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AnandkumarGeHsuKakadeandTelgarskyKeywordslatentvariablemodelstensordecompositionsmixturemodelstopicmodelsmethodofmomentspowermethod1IntroductionThemethodofmomentsisaclassicalparameterestima

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Tensor decompositions for learning latest variable models: Transcript

AnandkumarGeHsuKakadeandTelgarskyKeywordslatentvariablemodelstensordecompositionsmixturemodelstopicmodelsmethodofmomentspowermethod1IntroductionThemethodofmomentsisaclassicalparameterestima. IndexTopicarraytensor,1%*t%(tensor),1%t*%(tensor),1%t*t%(tensor),1aperm,2matmult,2tensor,14 Jiao Tong University . Shanghai, China . June 17, 2013 . The Bridges of Konigsberg . Euler, 1736. A Prescient Observation by Euler. Euler said: . The General Case. STA431: Spring 2013. See last slide for copyright information. An Extension of Multiple Regression. More than one regression-like equation. Includes latent variables. Variables can be explanatory in one equation and response in another. Based on the work with. Masafumi. . Fukuma. . and . Sotaro. . Sugishita. . (Kyoto Univ.). Naoya. . Umeda. . (Kyoto Univ.). [arXiv:1503.08812. ][JHEP . 1507 (2015) 088] . “. Random volumes from matrices. Shenghan Jiang. Boston College. Benasque. February. , 09, 2017. Symmetric tensor-networks and topological phases. Collaborators:. Ying Ran (Boston College) . Panjin. Kim, . Hyungyong. Lee, Jung . Hoon. He Zhang. 1. , He Huang. 2. , . Rui. Li. 1. , . Jie. Chen. 1. , Li-Shi Luo. 2. Jefferson Lab. Old Dominion University. FEIS-2, 05/15/2015. Outline. He Zhang. ---. 3. ---. Introduction of FMM. He Zhang. Trevor Linton, University of Utah. Acknowledgements. Thomas Henderson. Ross Whitaker. Tolga Tasdizen. The support of IAVO Research, Inc. through contract FA9550-08-C-005.. Field of Study. Geographical Information Systems. Author: Maximilian Nickel. Speaker: . Xinge. Wen. INTRODUCTION . –. Multi relational Data. Relational data is everywhere in our life:. WEB. Social networks. Bioinformatics. INTRODUCTION . –. Why Tensor . Author: Maximilian Nickel. Speaker: . Xinge. Wen. INTRODUCTION . –. Multi relational Data. Relational data is everywhere in our life:. WEB. Social networks. Bioinformatics. INTRODUCTION . –. Why Tensor . Overview. Theory. Basic . physics. Tensor. Diffusion . imaging . Practice. How . do you do DTI?. . Tractography. . DTI . in . FSL and other programs. Diffusion . Tensor Imaging. Brownian motion. near the ground states of nuclei. = Study by . 16. O(p,pd). 14. N reaction =. Isao Tanihata, H. J. Ong, s. Terashima and E443 collaboration at RCNP. IRCNPC and SPNEE, Beihang University, Beijing, China. 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”. Juan Andrés . Bazerque. , Gonzalo . Mateos. , and . Georgios. B. . Giannakis. . August. 8, 2012. . Spincom. group, University of Minnesota. . Acknowledgment: . AFOSR MURI grant no. FA 9550-10-1-0567. . SYFTET. Göteborgs universitet ska skapa en modern, lättanvänd och . effektiv webbmiljö med fokus på användarnas förväntningar.. 1. ETT UNIVERSITET – EN GEMENSAM WEBB. Innehåll som är intressant för de prioriterade målgrupperna samlas på ett ställe till exempel:.

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