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Random sequence
Results: 148

#	Item
51	Effect of Non-linear Deep Architecture in Sequence Labeling Mengqiu Wang MENGQIU @ CS . STANFORD . EDU Christopher D. Manning MANNING @ CS . STANFORD . EDU Add to Reading List Source URL: cs.stanford.edu Language: English - Date: 2013-05-15 05:53:10 Mathematics Science Computational neuroscience Conditional random field Theoretical computer science Perceptron Chunking Artificial neuron Supervised learning Neural networks Machine learning Statistics
52	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
53	Total Variation Reconstruction From Quasi-Random Samples Colas Schretter1,2 , Ignace Loris3 , Ann Dooms1,2 and Peter Schelkens1,2 1 Vrije Universiteit Brussel, Dept. of Electronics and Informatics, Pleinlaan 2, B-1050 Br Add to Reading List Source URL: homepages.ulb.ac.be Language: English - Date: 2014-02-26 11:10:13 Randomness Non-uniform random numbers Monte Carlo methods Numerical analysis Sampling Low-discrepancy sequence Peak signal-to-noise ratio Pseudo-random number sampling Inverse transform sampling Mathematics Applied mathematics Probability and statistics
54	Table of contents Summary .............................................................................................................................................. 3 Applications of random numbers.................. Add to Reading List Source URL: www.idquantique.com Language: English - Date: 2015-03-23 10:17:12 Random number generation Hardware random number generator Pseudorandomness Statistical randomness Pseudorandom number generator One-time pad Random sequence Fortuna Entropy Randomness Cryptography Applied mathematics
55	Assessing the significance of Sets of Words Assessing the significance of Sets of Words V. Boeva, J. Cl´ement, M. R´egnier and M. Vandenbogaert Moscow, Marne-la-Vall´ ee-CNRS, INRIA, Biozentrum Add to Reading List Source URL: www.cs.ucr.edu Language: English - Date: 2005-07-08 16:24:16 Sequence motif Random sequence Statistical hypothesis testing Normal distribution Randomness Statistics Bioinformatics Probability and statistics
56	Total Variation Reconstruction From Quasi-Random Samples ABSTRACT Pseudo-random numbers are often used for generating incoherent uniformly distributed sample distributions. Add to Reading List Source URL: homepages.ulb.ac.be Language: English - Date: 2014-11-17 05:12:41 Non-uniform random numbers Sampling Probability and statistics Randomness Numerical analysis Pseudorandomness Random sample Low-discrepancy sequence Pseudo-random number sampling Mathematics Statistics Applied mathematics
57	CDM (Pseudo-) Randomness Klaus Sutner Carnegie Mellon University Add to Reading List Source URL: www.idquantique.com Language: English - Date: 2015-03-23 10:17:13 Causality Random number generation Random sequence A Million Random Digits with 100 000 Normal Deviates Hardware random number generator Randomness extractor Randomness Cryptography Probability and statistics
58	REDEFINING RANDOMNESS QUANTIS WHEN RANDOM NUMBERS CANNOT BE LEFT TO CHANCE RANDOMNESS TEST REPORT Add to Reading List Source URL: www.idquantique.com Language: English - Date: 2015-03-23 10:17:12 Statistics Randomness tests Diehard tests Random number generation Pseudorandom number generator Wald–Wolfowitz runs test Pseudorandomness Random sequence Entropy Randomness Cryptography Probability and statistics
59	Semi-Supervised Conditional Random Fields for Improved Sequence Segmentation and Labeling Feng Jiao University of Waterloo Abstract We present a new semi-supervised training Add to Reading List Source URL: webdocs.cs.ualberta.ca Language: English - Date: 2006-12-28 15:24:48 Statistical theory Estimation theory Conditional random field Theoretical computer science Supervised learning Kullback–Leibler divergence Semi-supervised learning Expectation–maximization algorithm Conditional entropy Statistics Machine learning Information theory
60	Scholarly Document Information Extraction using Extensible Features for Efficient Higher Order Semi-CRFs Nguyen Viet Cuong, Muthu Kumar Chandrasekaran, Min-Yen Kan, Wee Sun Lee∗ Department of Computer Science, National Add to Reading List Source URL: aye.comp.nus.edu.sg Language: English - Date: 2015-04-23 05:59:28 Artificial intelligence Information extraction Applied mathematics Sequence labeling Parsing Learning Machine learning Conditional random field Theoretical computer science