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Text-to-Speech Synthesis

by Paul Taylor
"... ..."
Abstract - Cited by 32 (0 self) - Add to MetaCart
Abstract not found

KLEE: Unassisted and Automatic Generation of High-Coverage Tests for Complex Systems Programs

by Cristian Cadar, Daniel Dunbar, Dawson Engler
"... We present a new symbolic execution tool, KLEE, capable of automatically generating tests that achieve high coverage on a diverse set of complex and environmentally-intensive programs. We used KLEE to thoroughly check all 89 stand-alone programs in the GNU COREUTILS utility suite, which form the cor ..."
Abstract - Cited by 541 (14 self) - Add to MetaCart
We present a new symbolic execution tool, KLEE, capable of automatically generating tests that achieve high coverage on a diverse set of complex and environmentally-intensive programs. We used KLEE to thoroughly check all 89 stand-alone programs in the GNU COREUTILS utility suite, which form

Reversible Markov chains and random walks on graphs

by David Aldous, James Allen Fill , 2002
"... ..."
Abstract - Cited by 549 (13 self) - Add to MetaCart
Abstract not found

The Lifting Scheme: A Construction Of Second Generation Wavelets

by Wim Sweldens , 1997
"... . We present the lifting scheme, a simple construction of second generation wavelets, wavelets that are not necessarily translates and dilates of one fixed function. Such wavelets can be adapted to intervals, domains, surfaces, weights, and irregular samples. We show how the lifting scheme leads to ..."
Abstract - Cited by 541 (16 self) - Add to MetaCart
. We present the lifting scheme, a simple construction of second generation wavelets, wavelets that are not necessarily translates and dilates of one fixed function. Such wavelets can be adapted to intervals, domains, surfaces, weights, and irregular samples. We show how the lifting scheme leads

Three Generative, Lexicalised Models for Statistical Parsing

by Michael Collins , 1997
"... In this paper we first propose a new statistical parsing model, which is a generative model of lexicalised context-free gram- mar. We then extend the model to in- clude a probabilistic treatment of both subcategorisation and wh~movement. Results on Wall Street Journal text show that the parse ..."
Abstract - Cited by 567 (8 self) - Add to MetaCart
In this paper we first propose a new statistical parsing model, which is a generative model of lexicalised context-free gram- mar. We then extend the model to in- clude a probabilistic treatment of both subcategorisation and wh~movement. Results on Wall Street Journal text show

Exploiting Generative Models in Discriminative Classifiers

by Tommi Jaakkola, David Haussler - In Advances in Neural Information Processing Systems 11 , 1998
"... Generative probability models such as hidden Markov models provide a principled way of treating missing information and dealing with variable length sequences. On the other hand, discriminative methods such as support vector machines enable us to construct flexible decision boundaries and often resu ..."
Abstract - Cited by 538 (11 self) - Add to MetaCart
Generative probability models such as hidden Markov models provide a principled way of treating missing information and dealing with variable length sequences. On the other hand, discriminative methods such as support vector machines enable us to construct flexible decision boundaries and often

Pseudo-Random Generation from One-Way Functions

by Johan Håstad, Russell Impagliazzo, Leonid A. Levin, Michael Luby - PROC. 20TH STOC , 1988
"... Pseudorandom generators are fundamental to many theoretical and applied aspects of computing. We show howto construct a pseudorandom generator from any oneway function. Since it is easy to construct a one-way function from a pseudorandom generator, this result shows that there is a pseudorandom gene ..."
Abstract - Cited by 887 (22 self) - Add to MetaCart
Pseudorandom generators are fundamental to many theoretical and applied aspects of computing. We show howto construct a pseudorandom generator from any oneway function. Since it is easy to construct a one-way function from a pseudorandom generator, this result shows that there is a pseudorandom

DART: Directed automated random testing

by Patrice Godefroid, Nils Klarlund, Koushik Sen - In Programming Language Design and Implementation (PLDI , 2005
"... We present a new tool, named DART, for automatically testing software that combines three main techniques: (1) automated extraction of the interface of a program with its external environment using static source-code parsing; (2) automatic generation of a test driver for this interface that performs ..."
Abstract - Cited by 823 (41 self) - Add to MetaCart
We present a new tool, named DART, for automatically testing software that combines three main techniques: (1) automated extraction of the interface of a program with its external environment using static source-code parsing; (2) automatic generation of a test driver for this interface

Reversible jump Markov chain Monte Carlo computation and Bayesian model determination

by Peter J. Green - Biometrika , 1995
"... Markov chain Monte Carlo methods for Bayesian computation have until recently been restricted to problems where the joint distribution of all variables has a density with respect to some xed standard underlying measure. They have therefore not been available for application to Bayesian model determi ..."
Abstract - Cited by 1330 (24 self) - Add to MetaCart
determination, where the dimensionality of the parameter vector is typically not xed. This article proposes a new framework for the construction of reversible Markov chain samplers that jump between parameter subspaces of di ering dimensionality, which is exible and entirely constructive. It should therefore

Accurate whole human genome sequencing using reversible terminator chemistry. Nature 456: 53–59

by David R. Bentley, Shankar Balasubramanian, Harold P. Swerdlow, Geoffrey P. Smith, John Milton, Clive G. Brown, Kevin P. Hall, Dirk J. Evers, Colin L. Barnes, Helen R, Jonathan M. Boutell, Jason Bryant, Richard J. Carter, R. Keira Cheetham, Anthony J. Cox, Darren J. Ellis, Michael R. Flatbush, Niall A. Gormley, Sean J, Leslie J. Irving, Mirian S. Karbelashvili, Scott M. Kirk, Heng Li, Klaus S. Maisinger, Lisa J. Murray, Bojan Obradovic, Tobias Ost, Michael L, Mark R. Pratt, Isabelle M. J. Rasolonjatovo, Mark T. Reed, Roberto Rigatti, Chiara Rodighiero, Mark T. Ross, Andrea Sabot, Subramanian V. Sankar, Svilen S. Tzonev, Eric H. Vermaas, Klaudia Walter, Xiaolin Wu, Lu Zhang, Mohammed D. Alam, Carole Anastasi, Ify C. Aniebo, David M. D. Bailey, Iain R, Kevin F. Benson, Claire Bevis, Phillip J. Black, Asha Boodhun, Joe S. Brennan, A. Bridgham, Rob C. Brown, Andrew A. Brown, Dale H. Buermann, Abass A. Bundu, James C. Burrows, Nigel P. Carter, Nestor Castillo, Maria Chiara, E. Catenazzi, R. Neil Cooley, Natasha R. Crake, Olubunmi O. Dada, Konstantinos D, Belen Dominguez-fern, David J. Earnshaw, Ugonna C. Egbujor, David W. Elmore, Sergey S. Etchin, Mark R. Ewan, Milan Fedurco, Louise J. Fraser, Karin V. Fuentes Fajardo, W. Scott Furey, David George, Kimberley J. Gietzen, Colin P, George S. Golda, Philip A. Granieri, David E. Green, David L. Gustafson, Nancy F. Hansen, Kevin Harnish, Christian D. Haudenschild, Narinder I. Heyer, Matthew M. Hims, Johnny T. Ho, Adrian M. Horgan, Katya Hoschler, Steve Hurwitz, Denis V. Ivanov, Maria Q. Johnson, Terena James, T. A. Huw Jones, Tzvetana H. Kerelska, Alan D. Kersey, Irina Khrebtukova, Alex P. Kindwall, Paula I. Kokko-gonzales, Anil Kumar, Marc A. Laurent, Cynthia T. Lawley, Sarah E. Lee, Xavier Lee, Arnold K. Liao, Jennifer A. Loch, Mitch Lok, Shujun Luo, Radhika M. Mammen, John W. Martin, Patrick G. Mccauley, Paul Mcnitt, Parul Mehta, Keith W. Moon, Joe W. Mullens, Taksina Newington, Zemin Ning , 2008
"... ..."
Abstract - Cited by 620 (1 self) - Add to MetaCart
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