Results 1  10
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22
Coarsetofine nbest parsing and MaxEnt discriminative reranking
 In ACL
, 2005
"... Discriminative reranking is one method for constructing highperformance statistical parsers (Collins, 2000). A discriminative reranker requires a source of candidate parses for each sentence. This paper describes a simple yet novel method for constructing sets of 50best parses based on a co ..."
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Cited by 386 (14 self)
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Discriminative reranking is one method for constructing highperformance statistical parsers (Collins, 2000). A discriminative reranker requires a source of candidate parses for each sentence. This paper describes a simple yet novel method for constructing sets of 50best parses based on a coarsetofine generative parser (Charniak, 2000). This method generates 50best lists that are of substantially higher quality than previously obtainable.
Discriminative language modeling with conditional random fields and the perceptron algorithm
 In Proc. ACL
, 2004
"... This paper describes discriminative language modeling for a large vocabulary speech recognition task. We contrast two parameter estimation methods: the perceptron algorithm, and a method based on conditional random fields (CRFs). The models are encoded as deterministic weighted finite state automata ..."
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Cited by 61 (7 self)
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This paper describes discriminative language modeling for a large vocabulary speech recognition task. We contrast two parameter estimation methods: the perceptron algorithm, and a method based on conditional random fields (CRFs). The models are encoded as deterministic weighted finite state automata, and are applied by intersecting the automata with wordlattices that are the output from a baseline recognizer. The perceptron algorithm has the benefit of automatically selecting a relatively small feature set in just a couple of passes over the training data. However, using the feature set output from the perceptron algorithm (initialized with their weights), CRF training provides an additional 0.5 % reduction in word error rate, for a total 1.8 % absolute reduction from the baseline of 39.2%. 1
Objectoriented software for quadratic programming
 ACM Transactions on Mathematical Software
, 2001
"... The objectoriented software package OOQP for solving convex quadratic programming problems (QP) is described. The primaldual interior point algorithms supplied by OOQP are implemented in a way that is largely independent of the problem structure. Users may exploit problem structure by supplying li ..."
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Cited by 60 (2 self)
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The objectoriented software package OOQP for solving convex quadratic programming problems (QP) is described. The primaldual interior point algorithms supplied by OOQP are implemented in a way that is largely independent of the problem structure. Users may exploit problem structure by supplying linear algebra, problem data, and variable classes that are customized to their particular applications. The OOQP distribution contains default implementations that solve several important QP problem types, including general sparse and dense QPs, boundconstrained QPs, and QPs arising from support vector machines and Huber regression. The implementations supplied with the OOQP distribution are based on such well known linear algebra packages as MA27/57, LAPACK, and PETSc. OOQP demonstrates the usefulness of objectoriented design in optimization software development, and establishes standards that can be followed in the design of software packages for other classes of optimization problems. A number of the classes in OOQP may also be reusable directly in other codes.
A scalable modular convex solver for regularized risk minimization
 In KDD. ACM
, 2007
"... A wide variety of machine learning problems can be described as minimizing a regularized risk functional, with different algorithms using different notions of risk and different regularizers. Examples include linear Support Vector Machines (SVMs), Logistic Regression, Conditional Random Fields (CRFs ..."
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Cited by 59 (14 self)
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A wide variety of machine learning problems can be described as minimizing a regularized risk functional, with different algorithms using different notions of risk and different regularizers. Examples include linear Support Vector Machines (SVMs), Logistic Regression, Conditional Random Fields (CRFs), and Lasso amongst others. This paper describes the theory and implementation of a highly scalable and modular convex solver which solves all these estimation problems. It can be parallelized on a cluster of workstations, allows for datalocality, and can deal with regularizers such as ℓ1 and ℓ2 penalties. At present, our solver implements 20 different estimation problems, can be easily extended, scales to millions of observations, and is up to 10 times faster than specialized solvers for many applications. The open source code is freely available as part of the ELEFANT toolbox.
A Component Architecture for HighPerformance Scientific Computing
 Intl. J. HighPerformance Computing Applications
, 2004
"... The Common Component Architecture (CCA) provides a means for software developers to manage the complexity of largescale scientific simulations and to move toward a plugandplay environment for highperformance computing. In the scientific computing context, component models also promote collaborat ..."
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Cited by 46 (17 self)
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The Common Component Architecture (CCA) provides a means for software developers to manage the complexity of largescale scientific simulations and to move toward a plugandplay environment for highperformance computing. In the scientific computing context, component models also promote collaboration using independently developed software, thereby allowing particular individuals or groups to focus on the aspects of greatest interest to them. The CCA supports parallel and distributed computing as well as local highperformance connections between components in a languageindependent manner. The design places minimal requirements on components
PENNON  a code for convex nonlinear and semidefinite programming
 Optimization Methods and Software
"... We introduce a computer program PENNON for the solution of problems of convex Nonlinear and Semidefinite Programming (NLPSDP). The algorithm used in PENNON is a generalized version of the Augmented Lagrangian method, originally introduced by BenTal and Zibulevsky for convex NLP problems. We presen ..."
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Cited by 37 (9 self)
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We introduce a computer program PENNON for the solution of problems of convex Nonlinear and Semidefinite Programming (NLPSDP). The algorithm used in PENNON is a generalized version of the Augmented Lagrangian method, originally introduced by BenTal and Zibulevsky for convex NLP problems. We present generalization of this algorithm to convex NLPSDP problems, as implemented in PENNON and details of its implementation. The code can also solve secondorder conic programming (SOCP) problems, as well as problems with a mixture of SDP, SOCP and NLP constraints. Results of extensive numerical tests and comparison with other optimization codes are presented. The test examples show that PENNON is particularly suitable for large sparse problems. 1
Evaluation and Extension of Maximum Entropy Models with Inequality Constraints
, 2003
"... A maximum entropy (ME) model is usually estimated so that it conforms to equality constraints on feature expectations. ..."
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Cited by 25 (0 self)
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A maximum entropy (ME) model is usually estimated so that it conforms to equality constraints on feature expectations.
Parallel components for PDEs and optimization: Some issues and experiences
 PARALLEL COMPUTING
, 2002
"... Highperformance simulations in computational science often involve the combined software contributions of multidisciplinary teams of scientists, engineers, mathematicians, and computer scientists. One goal of componentbased software engineering in largescale scientific simulations is to help mana ..."
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Cited by 17 (6 self)
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Highperformance simulations in computational science often involve the combined software contributions of multidisciplinary teams of scientists, engineers, mathematicians, and computer scientists. One goal of componentbased software engineering in largescale scientific simulations is to help manage such complexity by enabling better interoperability among codes developed by different groups. This paper discusses recent work on building component interfaces and implementations in parallel numerical toolkits for mesh manipulations, discretization, linear algebra, and optimization. We consider several motivating applications involving partial differential equations and unconstrained minimization to demonstrate this approach and evaluate performance.
A differentiationenabled Fortran 95 compiler
 CODEN ACMSCU. ISSN 00983500 (print), 15577295 (electronic). 215 Tang:2005:DNI
, 2005
"... ..."
Using the GA and TAO toolkits for solving largescale optimization problems on parallel computers
"... Challenges in the scalable solution of largescale optimization problems include the development of innovative algorithms and efficient tools for parallel data manipulation. This paper discusses two complementary toolkits from the collection of Advanced CompuTational Software (ACTS), namely, Global ..."
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Cited by 7 (5 self)
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Challenges in the scalable solution of largescale optimization problems include the development of innovative algorithms and efficient tools for parallel data manipulation. This paper discusses two complementary toolkits from the collection of Advanced CompuTational Software (ACTS), namely, Global Arrays (GA) for parallel data management and the Toolkit for Advanced Optimization (TAO), which have been integrated to support largescale scientific applications of unconstrained and bound constrained minimization problems. Most likely to benefit are minimization problems arising in classical molecular dynamics, free energy simulations, and other applications where the coupling among variables requires dense data structures. TAO uses abstractions for vectors and matrices so that its optimization algorithms can easily interface to distributed data management and linear algebra capabilities implemented in the GA library. The GA/TAO interfaces are available both in the traditional library mode and as components compliant with the Common Component Architecture (CCA). We highlight the design of each toolkit, describe the interfaces between them, and demonstrate their use. Categories and Subject Descriptors: