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Graphical model
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#	Item
41	Hinge-loss Markov Random Fields: Convex Inference for Structured Prediction Stephen H. Bach Bert Huang Add to Reading List Source URL: psl.umiacs.umd.edu Language: English - Date: 2013-11-10 21:52:40 Graphical models Markov models Probability theory Markov random field Structured prediction Bayesian network Random field Markov chain Support vector machine Markov logic network Algorithm Hidden Markov model
42	Structural-RNN: Deep Learning on Spatio-Temporal Graphs Ashesh Jain1,2 , Amir R. Zamir2 , Silvio Savarese2 , and Ashutosh Saxena3 Deep Recurrent Neural Network architectures, though remarkably capable at modeling sequenc Add to Reading List Source URL: cvgl.stanford.edu Language: English - Date: 2016-04-30 18:59:32 Artificial neural networks Graph theory Artificial intelligence Recurrent neural network Computational neuroscience Computational statistics Deep learning Long short-term memory Connectivity Graphical model Graph Directed graph
43	Identifying Causal Effects with the R Package causaleffect Santtu Tikka Juha Karvanen Add to Reading List Source URL: cran.r-project.org Language: English - Date: 2016-02-23 03:40:29 Causal inference Regression analysis Graphical models Theoretical physics Multivariate statistics Causality Instrumental variable Confounding Causal model Causal structure Matching Topological sorting
44	Int. J. Human – Computer Studies , 185 – 213 External cognition: how do graphical representations work? MIKE SCAIFE AND YVONNE ROGERS School of Cognitiy e and Computing Sciences , Uniy ersity of Sussex , Add to Reading List Source URL: mcs.open.ac.uk Language: English - Date: 2006-10-27 04:13:40 Cognitive modeling Cognitive science Scientific modeling Thought Cognitive psychology Mental model Diagram Cognitive model Mental representation Object Process Methodology Solid modeling Distributed cognition
45	Efﬁcient Nonparametric Belief Propagation with Application to Articulated Body Tracking Tony X. Han Huazhong Ning Thomas S. Huang Beckman Institute and ECE Department Add to Reading List Source URL: www.ifp.illinois.edu Language: English - Date: 2006-11-17 00:18:56 Graphical models Probability distributions Belief propagation Coding theory Probability theory Conference on Computer Vision and Pattern Recognition Normal distribution Mixture model DNA polymerase lambda
46	Microsoft Word - WebSci11 flow final hok.doc Add to Reading List Source URL: www.websci11.org Language: English - Date: 2016-03-15 09:55:12 Humancomputer interaction Educational psychology Web design Marketing Technological change Webflow Technology acceptance model Flow Usability Educational technology Graphical user interface User experience
47	Context-aware Classification for Incremental Scene Interpretation Arne Kreutzmann Kasim Terzi´c Add to Reading List Source URL: w3.ualg.pt Language: English - Date: 2013-01-17 14:55:25 Statistical models Bayesian network Pattern recognition Hierarchy Graphical model Decision tree learning
48	Causal Analysis for Troubleshooting and Decision Support System Byoung Uk Kim, Sonia Vohnout, Esko Mikkola Mingyang Li, Jian Liu Add to Reading List Source URL: www.ridgetopgroup.com Language: English - Date: 2015-07-18 01:30:09 Networks Statistical models Bayesian network Causality Graph theory Bayesian inference Graphical model Causal model Network theory Directed acyclic graph Pattern recognition Dependency network
49	FROM GENE EXPRESSION TO MOLECULAR PATHWAYS T HESIS SUBMITTED FOR THE DEGREE OF “D OCTOR OF P HILOSOPHY ” BY Add to Reading List Source URL: www.cs.huji.ac.il Language: English - Date: 2015-08-10 08:23:34 Networks Statistical models Nir Friedman Gene regulatory network Bayesian network Thank You Algorithm Graphical model Causality
50	LEARNING HIDDEN VARIABLES IN PROBABILISTIC GRAPHICAL MODELS T HESIS SUBMITTED FOR THE DEGREE OF Add to Reading List Source URL: www.cs.huji.ac.il Language: English - Date: 2015-08-10 08:23:23 Statistical models Bayesian network Graphical model Nir Friedman Hidden variable theory Causality Expectationmaximization algorithm Expected value Latent variable