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    JMLR
    2012
    195views Programming Languages» more  JMLR 2012»
    Bayesian regularization of non-homogeneous dynamic Bayesian networks by globally coupling interaction parameters
    11 years 10 months ago
    Bayesian regularization of non-homogeneous dynamic Bayesian networks by globally coupling interaction parameters
    Download  jmlr.csail.mit.edu
    To relax the homogeneity assumption of classical dynamic Bayesian networks (DBNs), various recent studies have combined DBNs with multiple changepoint processes. The underlying as...
    Marco Grzegorczyk, Dirk Husmeier
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    UAI
    2003
    154views Artificial Intelligence» more  UAI 2003»
    Learning Continuous Time Bayesian Networks
    13 years 8 months ago
    Learning Continuous Time Bayesian Networks
    Download  robotics.stanford.edu
    Continuous time Bayesian networks (CTBN) describe structured stochastic processes with finitely many states that evolve over continuous time. A CTBN is a directed (possibly cycli...
    Uri Nodelman, Christian R. Shelton, Daphne Koller
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    ICML
    2004
    IEEE
    167views Machine Learning» more  ICML 2004»
    Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data
    14 years 8 months ago
    Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data
    Download  www.cs.umass.edu
    In sequence modeling, we often wish to represent complex interaction between labels, such as when performing multiple, cascaded labeling tasks on the same sequence, or when longra...
    Charles A. Sutton, Khashayar Rohanimanesh, Andrew ...
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    AI
    2010
    Springer
    164views Artificial Intelligence» more  AI 2010»
    Understanding the scalability of Bayesian network inference using clique tree growth curves
    13 years 7 months ago
    Understanding the scalability of Bayesian network inference using clique tree growth curves
    Download  ti.arc.nasa.gov
    Bayesian networks (BNs) are used to represent and ef ciently compute with multi-variate probability distributions in a wide range of disciplines. One of the main approaches to per...
    Ole J. Mengshoel
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    NECO
    2007
    150views more  NECO 2007»
    Reinforcement Learning, Spike-Time-Dependent Plasticity, and the BCM Rule
    13 years 7 months ago
    Reinforcement Learning, Spike-Time-Dependent Plasticity, and the BCM Rule
    Download  eprints.pascal-network.org
    Learning agents, whether natural or artificial, must update their internal parameters in order to improve their behavior over time. In reinforcement learning, this plasticity is ...
    Dorit Baras, Ron Meir
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