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    ACL
    2007
    125views Computational Linguistics» more  ACL 2007»
    A Seed-driven Bottom-up Machine Learning Framework for Extracting Relations of Various Complexity
    14 years 11 months ago
    A Seed-driven Bottom-up Machine Learning Framework for Extracting Relations of Various Complexity
    Download  www.dfki.de
    A minimally supervised machine learning framework is described for extracting relations of various complexity. Bootstrapping starts from a small set of n-ary relation instances as...
    Feiyu Xu, Hans Uszkoreit, Hong Li
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    100
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    NIPS
    2007
    142views Information Technology» more  NIPS 2007»
    Multi-Task Learning via Conic Programming
    14 years 11 months ago
    Multi-Task Learning via Conic Programming
    Download  sugiyama-www.cs.titech.ac.jp
    When we have several related tasks, solving them simultaneously is shown to be more effective than solving them individually. This approach is called multi-task learning (MTL) and...
    Tsuyoshi Kato, Hisashi Kashima, Masashi Sugiyama, ...
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    IJCAI
    2001
    163views Artificial Intelligence» more  IJCAI 2001»
    Exploiting Multiple Secondary Reinforcers in Policy Gradient Reinforcement Learning
    14 years 11 months ago
    Exploiting Multiple Secondary Reinforcers in Policy Gradient Reinforcement Learning
    Download  www.cs.colorado.edu
    Most formulations of Reinforcement Learning depend on a single reinforcement reward value to guide the search for the optimal policy solution. If observation of this reward is rar...
    Gregory Z. Grudic, Lyle H. Ungar
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    86
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    NIPS
    2004
    98views Information Technology» more  NIPS 2004»
    Learning Efficient Auditory Codes Using Spikes Predicts Cochlear Filters
    14 years 11 months ago
    Learning Efficient Auditory Codes Using Spikes Predicts Cochlear Filters
    Download  books.nips.cc
    The representation of acoustic signals at the cochlear nerve must serve a wide range of auditory tasks that require exquisite sensitivity in both time and frequency. Lewicki (2002...
    Evan C. Smith, Michael S. Lewicki
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    93
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    NIPS
    2000
    198views Information Technology» more  NIPS 2000»
    Learning Joint Statistical Models for Audio-Visual Fusion and Segregation
    14 years 11 months ago
    Learning Joint Statistical Models for Audio-Visual Fusion and Segregation
    Download  www.eecs.berkeley.edu
    People can understand complex auditory and visual information, often using one to disambiguate the other. Automated analysis, even at a lowlevel, faces severe challenges, includin...
    John W. Fisher III, Trevor Darrell, William T. Fre...
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