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Generative model
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71	Under review as a workshop contribution at ICLRG ENERATIVE CLASS - CONDITIONAL DENOISING AU - Add to Reading List Source URL: www.uoguelph.ca Language: English - Date: 2014-12-30 23:06:15 Probability and statistics Pattern recognition Markov chain Generative model Gibbs sampling Discriminative model Boltzmann machine Machine learning Statistics Computational statistics
72	Generative Modeling and Numerical Optimization for Energy Efficient Buildings - – energieinformatik 2013 – Add to Reading List Source URL: www.energieinformatik2013.at Language: English - Date: 2013-11-14 07:08:34 Operations research Computer graphics Heating ventilating and air conditioning Passive solar building design Sustainable architecture Non-uniform rational B-spline Geometric primitive Generative model Mathematical optimization Architecture 3D computer graphics Computer-aided design
73	THE MSR SYSTEM FOR IWSLT 2011 EVALUATION Xiaodong He, Amittai Axelrod1, Li Deng, Alex Acero, Mei-Yuh Hwang, Alisa Nguyen2, Andrew Wang3, Xiahui Huang4 Microsoft Research One Microsoft Way, Redmond, WA 98052 {xiaohe, deng Add to Reading List Source URL: research.microsoft.com Language: English - Date: 2012-04-18 03:35:07 Natural language processing Artificial intelligence Statistical natural language processing Machine learning Computer-assisted translation Statistical machine translation BLEU Evaluation of machine translation Generative model Computational linguistics Machine translation Science
74	Journal of Machine Learning Research–1816 Submitted 4/05; Revised 10/05; PublishedProbabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models Add to Reading List Source URL: jmlr.csail.mit.edu Language: English - Date: 2005-11-20 10:42:17 Principal component analysis Expectation–maximization algorithm Gaussian process Generative topographic map Covariance matrix Mixture model Conjugate prior Factor analysis Covariance function Statistics Multivariate statistics Kernel principal component analysis
75	Hidden Conditional Random Fields for Phone Recognition Yun-Hsuan Sung 1 and Dan Jurafsky 2 1 Electrical Engineering, Stanford University Add to Reading List Source URL: nlp.stanford.edu Language: English - Date: 2010-01-07 17:30:55 Probability and statistics Probability Hidden Markov model Conditional random field Speech recognition Mutual information Sequence labeling Generative model Viterbi algorithm Statistics Machine learning Markov models
76	Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs Vikash K. Mansinghka⇤ 1,2 Add to Reading List Source URL: papers.nips.cc Language: English - Date: 2014-04-23 13:05:14 Statistical models Bayesian inference Statistical forecasting Statistical inference Generative model Approximate Bayesian computation Pattern recognition Inference One-shot learning Statistics Bayesian statistics Machine learning
77	DISCRIMINATIVE TEMPLATE EXTRACTION FOR DIRECT MODELING Shankar Shivappa ∗ University of California, San Diego Dept. of Electrical and Computer Eng. La Jolla, CA 92093, USA Add to Reading List Source URL: research.microsoft.com Language: English - Date: 2010-01-07 16:01:16 Computational linguistics Automatic identification and data capture Computer accessibility Speech recognition Template Generative model Humanâ€“computer interaction Computing Software engineering Machine learning
78	Structured Generative Models of Natural Source Code Chris J. Maddison† University of Toronto CMADDIS @ CS . TORONTO . EDU Add to Reading List Source URL: jmlr.org Language: English - Date: 2015-02-01 17:03:28 Graphical models Bayesian statistics Machine learning Tree traversal Generative model Bayesian network Latent variable Hidden Markov model N-gram Statistics Statistical models Probability
79	STRUCTURALLY DISCRIMINATIVE GRAPHICAL MODELS FOR AUTOMATIC SPEECH RECOGNITION – RESULTS FROM THE 2001 JOHNS HOPKINS SUMMER WORKSHOP Add to Reading List Source URL: research.microsoft.com Language: English - Date: 2005-03-03 12:04:50 Graphical models Networks Machine learning Bayesian network Speech recognition Mutual information Expectation–maximization algorithm Hidden Markov model Generative model Statistics Statistical models Bayesian statistics
80	CLASSIFICATION AND RECOGNITION WITH DIRECT SEGMENT MODELS Geoffrey Zweig Microsoft Research ABSTRACT Segment based direct models have recently been used to improve the output of existing state-of-the Add to Reading List Source URL: research.microsoft.com Language: English - Date: 2012-01-17 14:29:12 Machine learning Computational linguistics Statistical models Graphical models Speech recognition N-gram Generative model Conditional random field Segmentation Science Statistics Probability and statistics

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