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Input Modeling
, 2003
"... Most discreteevent simulation models have stochastic elements that mimic the probabilistic nature of the system under consideration. A close match between the input model and the true underlying probabilistic mechanism associated with the system is required for successful input modeling. The genera ..."
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Cited by 7 (0 self)
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Most discreteevent simulation models have stochastic elements that mimic the probabilistic nature of the system under consideration. A close match between the input model and the true underlying probabilistic mechanism associated with the system is required for successful input modeling
Input Modeling
, 1998
"... Discreteevent simulation models typically have stochastic components that mimic the probabilistic nature of the system under consideration. Successful input modeling requires a close match between the input model and the true underlying probabilistic mechanism associated with the system. The genera ..."
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Cited by 7 (1 self)
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Discreteevent simulation models typically have stochastic components that mimic the probabilistic nature of the system under consideration. Successful input modeling requires a close match between the input model and the true underlying probabilistic mechanism associated with the system
Input Modeling
, 2000
"... Discreteevent simulation models typically have stochastic elements that mimic the probabilistic nature of the system under consideration. Successful input modeling requires a close match between the input model and the true underlying probabilistic mechanism associated with the system. The general ..."
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Discreteevent simulation models typically have stochastic elements that mimic the probabilistic nature of the system under consideration. Successful input modeling requires a close match between the input model and the true underlying probabilistic mechanism associated with the system. The general
Hierarchical Models of Object Recognition in Cortex
, 1999
"... The classical model of visual processing in cortex is a hierarchy of increasingly sophisticated representations, extending in a natural way the model of simple to complex cells of Hubel and Wiesel. Somewhat surprisingly, little quantitative modeling has been done in the last 15 years to explore th ..."
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Cited by 817 (84 self)
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predictions. The model is based on a novel MAXlike operation on the inputs to certain cortical neurons which may have a general role in cortical function.
Active Learning with Statistical Models
, 1995
"... For manytypes of learners one can compute the statistically "optimal" way to select data. We review how these techniques have been used with feedforward neural networks [MacKay, 1992# Cohn, 1994]. We then showhow the same principles may be used to select data for two alternative, statist ..."
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Cited by 677 (12 self)
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For manytypes of learners one can compute the statistically "optimal" way to select data. We review how these techniques have been used with feedforward neural networks [MacKay, 1992# Cohn, 1994]. We then showhow the same principles may be used to select data for two alternative, statisticallybased learning architectures: mixtures of Gaussians and locally weighted regression. While the techniques for neural networks are expensive and approximate, the techniques for mixtures of Gaussians and locally weighted regression are both efficient and accurate.
A distributed, developmental model of word recognition and naming
 PSYCHOLOGICAL REVIEW
, 1989
"... A parallel distributed processing model of visual word recognition and pronunciation is described. The model consists of sets of orthographic and phonological units and an interlevel of hidden units. Weights on connections between units were modified during a training phase using the backpropagatio ..."
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Cited by 675 (48 self)
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is simulated without pronunciation rules, and lexical decisions are simulated without accessing wordlevel representations. The performance of the model is largely determined by three factors: the nature of the input, a significant fragment of written English; the learning rule, which encodes the implicit
Bandera: Extracting Finitestate Models from Java Source Code
 IN PROCEEDINGS OF THE 22ND INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING
, 2000
"... Finitestate verification techniques, such as model checking, have shown promise as a costeffective means for finding defects in hardware designs. To date, the application of these techniques to software has been hindered by several obstacles. Chief among these is the problem of constructing a fini ..."
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Cited by 653 (35 self)
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program source code. Bandera takes as input Java source code and generates a program model in the input language of one of several existing verification tools; Bandera also maps verifier outputs back to the original source code. We discuss the major components of Bandera and give an overview of how it can
Fitting a mixture model by expectation maximization to discover motifs in biopolymers
 Proceedings of the Second International Conference on Intelligent Systems for Molecular Biology
, 1994
"... ABSTRACT: The algorithm described in this paper discovers one or more motifs in a collection of DNA or protein sequences by using the technique of expectation maximization to fit a twocomponent finite mixture model to the set of sequences. Multiple motifs are found by fitting a twocomponent finite ..."
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Cited by 941 (5 self)
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component finite mixture model to the data, probabilistically erasing the occurrences of the motif thus found, and repeating the process to find successive motifs. The algorithm requires only a set of sequences and a number specifying the width of the motifs as input. It returns a model of each motif and a
Results 1  10
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1,946,308