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Kullback–Leibler divergence
Results: 486

#	Item
271	CONTENT-BASED MUSICAL SIMILARITY COMPUTATION USING THE HIERARCHICAL DIRICHLET PROCESS Matthew Hoffman Princeton University Dept. of Computer Science Add to Reading List Source URL: www.cs.princeton.edu Language: English - Date: 2010-08-18 22:20:55 Dirichlet process Hidden Markov model Mixture model Expectation–maximization algorithm Information retrieval Dirichlet distribution Normal distribution Semantic similarity Kullback–Leibler divergence Statistics Machine learning Natural language processing
272	Nonparametric Variational Inference Samuel J. Gershman [removed] Department of Psychology, Princeton University, Green Hall, Princeton, NJ[removed]USA Matthew D. Hoffman Add to Reading List Source URL: www.cs.princeton.edu Language: English - Date: 2012-06-05 15:28:29 Estimation theory Statistical models Statistical theory Variational Bayesian methods Probability theory Conjugate prior Mixture model Marginal likelihood Kullback–Leibler divergence Statistics Bayesian statistics Probability and statistics
273	Boosting with Incomplete Information Gholamreza Haffari1∗ Yang Wang1∗ Shaojun Wang2 Greg Mori1 Add to Reading List Source URL: www.cs.sfu.ca Language: English - Date: 2008-05-20 17:07:06 Ensemble learning Supervised learning Kullback–Leibler divergence Margin classifier AdaBoost Quasigroup Mutual information Lambda calculus Statistics Mathematics Machine learning
274	Expressive Power of Conditional Restricted Boltzmann Machines Guido Montúfar Nihat Ay Keyan Ghazi-Zahedi Add to Reading List Source URL: www.santafe.edu Language: English - Date: 2014-07-31 14:06:57 Mathematical analysis Conditioning Conditional probability Joint probability distribution Mutual information Kullback–Leibler divergence Statistics Probability theory Probability and statistics
275	Variational Inference for the Nested Chinese Restaurant Process Chong Wang Computer Science Department Princeton University Add to Reading List Source URL: www.cs.princeton.edu Language: English - Date: 2010-08-18 22:20:55 Variational Bayesian methods Statistical models Expectation–maximization algorithm Dirichlet process Gibbs sampling Mixture model Bayesian inference Exponential family Kullback–Leibler divergence Statistics Bayesian statistics Statistical theory
276	Mixed Membership Stochastic Blockmodels Edoardo M. Airoldi 1,2 , David M. Blei 1 , Stephen E. Fienberg 3,4 & Eric P. Xing 4∗ 1 Department of Computer Science, 2 Lewis-Sigler Institute, Princeton University 3 Add to Reading List Source URL: www.cs.princeton.edu Language: English - Date: 2010-08-18 22:20:56 Missing data Bayesian statistics Kalman filter Latent Dirichlet allocation Constructible universe FO Kullback–Leibler divergence Variational Bayesian methods Statistics Estimation theory Expectation–maximization algorithm
277	IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 35, NO. X, XXXXXXX 2013 Add to Reading List Source URL: www.cs.toronto.edu Language: English - Date: 2013-07-18 13:55:37 One-shot learning Anglo-Saxon runes Kullback–Leibler divergence Bar Autoregressive conditional heteroskedasticity Thorn Statistics Latin script DBm
278	Learning Stochastic Feedforward Neural Networks Yichuan Tang Department of Computer Science University of Toronto Toronto, Ontario, Canada. Add to Reading List Source URL: www.cs.toronto.edu Language: English - Date: 2013-11-19 21:06:13 Machine learning Estimation theory Statistical theory Expectation–maximization algorithm Bayesian network Gibbs sampling Perceptron Kullback–Leibler divergence Mixture model Statistics Statistical models Neural networks
279	Journal of Machine Learning Research[removed]1031 Submitted 9/12; Revised 1/13; Published 4/13 Variational Inference in Nonconjugate Models Chong Wang Add to Reading List Source URL: www.cs.princeton.edu Language: English - Date: 2013-06-14 05:31:44 Estimation theory Gamma distribution Conjugate prior Exponential family Marginal likelihood Delta method Sufficient statistic Kullback–Leibler divergence Expectation–maximization algorithm Statistics Statistical theory Bayesian statistics
280	Decomposing Spatiotemporal Brain Patterns into Topographic Latent Sources Samuel J. Gershman∗ Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA Add to Reading List Source URL: www.cs.princeton.edu Language: English - Date: 2014-05-19 05:49:39 Bayesian statistics Statistical theory Kullback–Leibler divergence Thermodynamics Identifiability Marginal likelihood Gamma distribution Functional magnetic resonance imaging Markov chain Statistics Logic Estimation theory

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