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    EACL
    2009
    ACL Anthology
    108views Natural Language Processing» more  EACL 2009»
    Cube Summing, Approximate Inference with Non-Local Features, and Dynamic Programming without Semirings
    15 years 10 months ago
    Cube Summing, Approximate Inference with Non-Local Features, and Dynamic Programming without Semirings
    Download  www.cs.cmu.edu
    We introduce cube summing, a technique that permits dynamic programming algorithms for summing over structures (like the forward and inside algorithms) to be extended with non-loc...
    Kevin Gimpel, Noah A. Smith
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    NIPS
    1998
    164views Information Technology» more  NIPS 1998»
    Approximate Learning of Dynamic Models
    14 years 11 months ago
    Approximate Learning of Dynamic Models
    Download  robotics.stanford.edu
    Inference is a key component in learning probabilistic models from partially observable data. When learning temporal models, each of the many inference phases requires a complete ...
    Xavier Boyen, Daphne Koller
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    BMCBI
    2007
    164views more  BMCBI 2007»
    Comparison of probabilistic Boolean network and dynamic Bayesian network approaches for inferring gene regulatory networks
    14 years 9 months ago
    Comparison of probabilistic Boolean network and dynamic Bayesian network approaches for inferring gene regulatory networks
    Download  www.biomedcentral.com
    Background: The regulation of gene expression is achieved through gene regulatory networks (GRNs) in which collections of genes interact with one another and other substances in a...
    Peng Li, Chaoyang Zhang, Edward J. Perkins, Ping G...
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    BMCBI
    2010
    112views more  BMCBI 2010»
    A parameter-adaptive dynamic programming approach for inferring cophylogenies
    14 years 9 months ago
    A parameter-adaptive dynamic programming approach for inferring cophylogenies
    Download  www.biomedcentral.com
    Background: Coevolutionary systems like hosts and their parasites are commonly used model systems for evolutionary studies. Inferring the coevolutionary history based on given phy...
    Daniel Merkle, Martin Middendorf, Nicolas Wieseke
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    BIOINFORMATICS
    2006
    118views more  BIOINFORMATICS 2006»
    A probabilistic dynamical model for quantitative inference of the regulatory mechanism of transcription
    14 years 9 months ago
    A probabilistic dynamical model for quantitative inference of the regulatory mechanism of transcription
    Download  eprints.pascal-network.org
    Motivation Quantitative estimation of the regulatory relationship between transcription factors and genes is a fundamental stepping stone when trying to develop models of cellular...
    Guido Sanguinetti, Magnus Rattray, Neil D. Lawrenc...
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