SA Abstract Sever al metho ds for identifying individual motif instanc by exhaustive evaluation of mers 10 ar applie to the ole Upstr am gions USR of al 4289 Escherichia oli ORFs Instanc es of the ShineDalgarno SD site ar adily identi57356e using the ID: 4949 Download Pdf
uciedu Abstract Sample estimates of moments and cumulants are known to be unstable in the presence of outliers This problem is especially severe for higher order statistics like kurtosis which are used in algo rithms for independent components analys
uciedu Yee Whye Teh Gatsby Computational Neuroscience Unit University College London London UK ywtehgatsbyuclacuk Abstract Latent Dirichlet analysis or topic modeling is a 64258exible latent variable framework for model ing highdimensional sparse cou
Experiment I used uniform time scaling to match the speaking rate between clear and conversational speech Experiment II decreased the speaking rate in conversational speech without processing artifacts by increasing silent gaps between phonetic segm
uciedu Abstract We describe an algorithm for learning bilinear SVMs Bilinear classi64257ers are a discriminative instantiation of bilinear models that capture the dependence of data on multiple factors Such models are particularly appropriate for vis
MSSVM properly accounts for the uncertainty of hidden variables and can sig ni64257cantly outperform the previously proposed la tent structured SVM LSSVM Yu Joachims 2009 and other stateofart methods especially when that uncertainty is large Our m
uciedu Padhraic Smyth Department of Computer Science University of California Irvine smythicsuciedu ABSTRACT Locationbased data is increasingly prevalent with the rapid increase and adoption of mobile devices In this paper we address the problem of l
In this connection w ein tro duce a class NL T of functions computable in ne arly line ar time log 1 on random access computers NL is ery robust and do es not dep end on the particular c hoice of random access computers Kolmogoro v mac hines Sc h on
b erk eley edu ttpwwwcsb erk eley edu milc Bhask ara Marthi Computer Science Division Univ ersit of California at Berk eley USA bhask aracsb erk eley edu ttpwwwcsb erk eley edu bhask ara Stuart Russell Computer Science Division Univ ersit of Californ
e prop ose a new approac h to the corresp ondence prob lem that mak es use of nonparametric lo cal transforms as the basis for correlation Nonparametric lo cal transforms rely on the relativ eorder ing of lo cal in tensit yv alues and not on the in
Principal Direction Divisiv Daniel Boley y Departmen tof Computer Science and Engineering Univ ersit yofMinnesota 200 UnionStreetS.E., Rm 4-192 Minneapolis, 55455, USA Abstract W e prop ose a new al
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SA Abstract Sever al metho ds for identifying individual motif instanc by exhaustive evaluation of mers 10 ar applie to the ole Upstr am gions USR of al 4289 Escherichia oli ORFs Instanc es of the ShineDalgarno SD site ar adily identi57356e using the
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