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Latent learning
Results: 355

#	Item
101	Weakly Supervised Learning of Mid-Level Features with Beta-Bernoulli Process Restricted Boltzmann Machines Roni Mittelman, Honglak Lee, Benjamin Kuipers, Silvio Savarese Department of Electrical Engineering and Computer Add to Reading List Source URL: cvgl.stanford.edu Language: English - Date: 2014-04-08 01:52:48 Attribute grammar Software engineering Computing Computer programming Boltzmann machine Programming paradigms Latent Dirichlet allocation
102	Learning Overcomplete Latent Variable Models through Tensor Decompositions Add to Reading List Source URL: spectrallearning.github.io Language: English - Date: 2014-07-01 18:01:34 Linear algebra Rank Ricci decomposition Algebra Tensors Mathematics
103	Supervised Semantic Indexing Bing Bai, Jason Weston, Ronan Collobert, and David Grangier NEC Labs America, 4 Independence Way, Princeton, USA 08540 {bbai,jasonw,collober,dgrangier}@nec-labs.com Add to Reading List Source URL: ronan.collobert.com Language: English - Date: 2009-04-16 18:20:30 Vector space model Latent semantic analysis Tf*idf Latent semantic indexing Probabilistic latent semantic analysis Learning to rank Latent Dirichlet allocation Query expansion Search engine indexing Information science Information retrieval Science
104	Supervised Semantic Indexing Bing Bai† , Jason Weston† , David Grangier† , Ronan Collobert† , Yanjun Qi† , Kunihiko Sadamasa† , Olivier Chapelle? , Kilian Weinberger? † NEC Labs America, Princeton NJ, USA ? Add to Reading List Source URL: ronan.collobert.com Language: English - Date: 2009-09-03 12:22:35 Vector space model Latent semantic analysis Latent semantic indexing Tf*idf Search engine indexing Learning to rank Relevance feedback Hash function Probabilistic latent semantic analysis Information science Information retrieval Science
105	Large-Scale Feature Learning With Spike-and-Slab Sparse Coding Ian J. Goodfellow Aaron Courville Yoshua Bengio DIRO, Universit´e de Montr´eal, Montr´eal, Qu´ebec, Canada Add to Reading List Source URL: icml.cc Language: English - Date: 2012-06-07 13:20:32 Statistical models Bayesian networks Learning Expectation–maximization algorithm Canadian Institute for Advanced Research Supervised learning Unsupervised learning Feature selection Latent variable Statistics Machine learning Cybernetics
106	Noname manuscript No. (will be inserted by the editor) Learning to Rank with (a Lot of ) Word Features Bing Bai · Jason Weston · David Grangier · Ronan Collobert · Kunihiko Sadamasa · Add to Reading List Source URL: ronan.collobert.com Language: English - Date: 2009-09-30 13:22:01 Vector space model Learning to rank Ranking SVM Tf*idf Hash function Search engine indexing N-gram Supervised learning Latent semantic indexing Information science Information retrieval Science
107	1. Introduction 2. DLCA 3. Example with social capital Add to Reading List Source URL: www.modeling.uconn.edu Language: English - Date: 2015-05-26 13:12:19 Latent class model Machine learning Mixture model Psychometrics Statistics Categorical data Statistical models
108	A Hybrid Text Classification Approach for Analysis of Student Essays Carolyn P. Ros´e, Antonio Roque, Dumisizwe Bhembe, Kurt Vanlehn Learning Research and Development Center, University of Pittsburgh, 3939 O’Hara St., Add to Reading List Source URL: aroque.bol.ucla.edu Language: English - Date: 2010-11-21 01:05:04 Artificial intelligence Latent semantic analysis Preposition and postposition Bag of words model ACT-R Question Information retrieval Natural language processing Linguistics Science
109	The Grouped Author-Topic Model for Unsupervised Entity Resolution Andrew M. Dai and Amos J. Storkey Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, U.K. {a.dai,a.storkey}@ed Add to Reading List Source URL: homepages.inf.ed.ac.uk Language: English - Date: 2011-09-27 08:11:22 Statistical natural language processing Stochastic processes Machine learning Statistical models Natural language processing Latent Dirichlet allocation Dirichlet process Plate notation Mixture model Statistics Probability and statistics Probability
110	A Gaussian Latent Variable Model for Large Margin Classification of Labeled and Unlabeled Data Do-kyum Kim, Matthew Der and Lawrence K. Saul Department of Computer Science and Engineering, University of California, San D Add to Reading List Source URL: cseweb.ucsd.edu Language: English - Date: 2014-02-24 20:30:41 Estimation theory Learning Statistical theory Semi-supervised learning Expectation–maximization algorithm Supervised learning Support vector machine Maximum likelihood Stability Statistics Machine learning Statistical classification