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Markov random field
Results: 325

#	Item
21	Marginals-to-Models Reducibility Michael Kearns University of Pennsylvania Add to Reading List Source URL: theory.stanford.edu Language: English - Date: 2013-11-08 17:46:03 Mathematics Computational complexity theory Graph theory Graphical models Combinatorial optimization Convex optimization Operations research Probability theory Polynomial Ellipsoid method Markov random field Bayesian network
22	Low Level Vision via Switchable Markov Random Fields Dahua Lin CSAIL, MIT John Fisher CSAIL, MIT Add to Reading List Source URL: dahua.me Language: English - Date: 2013-01-06 22:24:23 Statistics Artificial intelligence Computer vision Graphical models Machine learning Image processing Image segmentation Bayesian statistics Markov random field Conference on Computer Vision and Pattern Recognition Mixture model Noise reduction
23	Learning Latent Groups with Hinge-loss Markov Random Fields Stephen H. Bach Bert Huang Lise Getoor University of Maryland, College Park, Maryland 20742, USA Add to Reading List Source URL: stephenbach.net Language: English - Date: 2013-06-14 15:37:03 Statistics Probability theory Graphical models Machine learning Statistical models Statistical theory Bayesian statistics Markov random field Latent variable Expectationmaximization algorithm Marginal likelihood Conditional random field
24	Modelling Gaussian Fields and Geostatistical Data Using Gaussian Markov Random Fields Outline 1. Introduction 2. Geostatistical Models and Gaussian Markov Random Fields Add to Reading List Source URL: evavivalt.com Language: English - Date: 2014-11-11 20:22:18 Geostatistics Statistics Data Probability theory Variogram Covariance function Stationary process Random field Time series Markov random field Ising model Covariance
25	1 Brain Anatomical Structure Segmentation by Hybrid Discriminative/Generative Models Zhuowen Tu, Katherine L. Narr, Piotr Doll´ar, Ivo Dinov, Paul M. Thompson, and Arthur W. Toga Add to Reading List Source URL: pages.ucsd.edu Language: English - Date: 2007-08-09 13:56:19 Artificial intelligence Machine learning Learning Graphical models Image segmentation Discriminative model Principal component analysis Conditional random field AdaBoost Functional magnetic resonance imaging Markov random field Bag-of-words model in computer vision
26	Collective Activity Detection using Hinge-loss Markov Random Fields Ben London, Sameh Khamis, Stephen H. Bach, Bert Huang, Lise Getoor, Larry Davis University of Maryland College Park, MD 20742 {blondon,sameh,bach,bert,g Add to Reading List Source URL: stephenbach.net Language: English - Date: 2013-06-10 18:15:10 Artificial intelligence Probability Statistics Bayesian statistics Markov networks Graphical models Markov random field Probability theory Image segmentation Probabilistic soft logic Activity recognition Support vector machine
27	STRUCTURED DISCRIMINATIVE MODELS USING DEEP NEURAL-NETWORK FEATURES R. C. van Dalen, J. Yang, H. Wang, A. Ragni, C. Zhang, M. J. F. Gales Department of Engineering, University of Cambridge, United Kingdom ABSTRACT State- Add to Reading List Source URL: mi.eng.cam.ac.uk Language: English - Date: 2016-07-12 11:46:12 Markov models Machine learning Artificial intelligence Learning Computational linguistics Speech recognition software Computer accessibility Speech recognition Hidden Markov model Conditional random field Language model Image segmentation
28	Structured and Infinite Discriminative Models for Speech Recognition Jingzhou Yang Homerton College Add to Reading List Source URL: mi.eng.cam.ac.uk Language: English - Date: 2016-07-26 12:00:52 Statistics Learning Artificial intelligence Machine learning Statistical models Statistical inference Graphical models Structured prediction Discriminative model Conditional random field Generative model Hidden Markov model
29	Primal Sketch: Integrating Structure and Texture ? Cheng-en Guo, Song-Chun Zhu, and Ying Nian Wu Departments of Statistics and Computer Science University of California, Los Angeles Add to Reading List Source URL: www.stat.ucla.edu Language: English Signal processing Image processing Linear filters Filter theory Feature detection Gabor filter Image segmentation Markov random field Edge detection Filter Canny edge detector Simple cell
30	Perturb-and-MAP Random Fields 8. Example: Learning an Ising Model 5. Perturb-and-MAP: Main Idea Add to Reading List Source URL: www.stat.ucla.edu Language: English - Date: 2011-11-04 05:05:53 Image segmentation Lattice models Statistical mechanics Markov random field Ising model

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