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    DATE
    2005
    IEEE
    99views Hardware» more  DATE 2005»
    Worst-Case and Average-Case Analysis of n-Detection Test Sets
    14 years 1 months ago
    Worst-Case and Average-Case Analysis of n-Detection Test Sets
    Download  www.date-conference.com
    Test sets that detect each target fault n times (n-detection test sets) are typically generated for restricted values of n due to the increase in test set size with n. We perform ...
    Irith Pomeranz, Sudhakar M. Reddy
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    SAC
    2008
    ACM
    128views Applied Computing» more  SAC 2008»
    PHALANX: a graph-theoretic framework for test case prioritization
    13 years 7 months ago
    PHALANX: a graph-theoretic framework for test case prioritization
    Download  vorlon.case.edu
    Test case prioritization for regression testing can be performed using different metrics (e.g., statement coverage, path coverage) depending on the application context. Employing ...
    Murali Krishna Ramanathan, Mehmet Koyutürk, A...
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    ESANN
    2008
    150views Neural Networks» more  ESANN 2008»
    Conditional prediction of time series using spiral recurrent neural network
    13 years 9 months ago
    Conditional prediction of time series using spiral recurrent neural network
    Download  www.dice.ucl.ac.be
    Frequently, sequences of state transitions are triggered by specific signals. Learning these triggered sequences with recurrent neural networks implies storing them as different at...
    Huaien Gao, Rudolf Sollacher
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    ESANN
    2008
    127views Neural Networks» more  ESANN 2008»
    Using graph-theoretic measures to predict the performance of associative memory models
    13 years 9 months ago
    Using graph-theoretic measures to predict the performance of associative memory models
    Download  www.dice.ucl.ac.be
    We test a selection of associative memory models built with different connection strategies, exploring the relationship between the structural properties of each network and its pa...
    Lee Calcraft, Rod Adams, Weiliang Chen, Neil Davey
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    NIPS
    2001
    112views Information Technology» more  NIPS 2001»
    A Bayesian Model Predicts Human Parse Preference and Reading Times in Sentence Processing
    13 years 9 months ago
    A Bayesian Model Predicts Human Parse Preference and Reading Times in Sentence Processing
    Download  www.stanford.edu
    Narayanan and Jurafsky (1998) proposed that human language comprehension can be modeled by treating human comprehenders as Bayesian reasoners, and modeling the comprehension proce...
    S. Narayanan, Daniel Jurafsky
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