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    PKDD
    2009
    Springer
    109views Data Mining» more  PKDD 2009»
    Learning Multi-linear Representations of Distributions for Efficient Inference
    14 years 5 months ago
    Learning Multi-linear Representations of Distributions for Efficient Inference
    Download  l2r.cs.uiuc.edu
    Dan Roth, Rajhans Samdani
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    JMLR
    2010
    134views more  JMLR 2010»
    Using Contextual Representations to Efficiently Learn Context-Free Languages
    13 years 5 months ago
    Using Contextual Representations to Efficiently Learn Context-Free Languages
    Download  jmlr.csail.mit.edu
    We present a polynomial update time algorithm for the inductive inference of a large class of context-free languages using the paradigm of positive data and a membership oracle. W...
    Alexander Clark, Rémi Eyraud, Amaury Habrar...
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    ICML
    2008
    IEEE
    157views Machine Learning» more  ICML 2008»
    Efficiently learning linear-linear exponential family predictive representations of state
    14 years 11 months ago
    Efficiently learning linear-linear exponential family predictive representations of state
    Download  web.mit.edu
    Exponential Family PSR (EFPSR) models capture stochastic dynamical systems by representing state as the parameters of an exponential family distribution over a shortterm window of...
    David Wingate, Satinder P. Singh
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    ICML
    2006
    IEEE
    143views Machine Learning» more  ICML 2006»
    Efficient inference on sequence segmentation models
    14 years 11 months ago
    Efficient inference on sequence segmentation models
    Download  www.it.iitb.ac.in
    Sequence segmentation is a flexible and highly accurate mechanism for modeling several applications. Inference on segmentation models involves dynamic programming computations tha...
    Sunita Sarawagi
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    ICML
    2009
    IEEE
    309views Machine Learning» more  ICML 2009»
    Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
    14 years 11 months ago
    Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
    Download  www.cnbc.cmu.edu
    There has been much interest in unsupervised learning of hierarchical generative models such as deep belief networks. Scaling such models to full-sized, high-dimensional images re...
    Honglak Lee, Roger Grosse, Rajesh Ranganath, Andre...
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