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    CVPR
    2012
    IEEE
    201views Computer Vision» more  CVPR 2012»
    Nonparametric discovery of activity patterns from video collections
    11 years 11 months ago
    Nonparametric discovery of activity patterns from video collections
    Download  www.cs.brown.edu
    We propose a nonparametric framework based on the beta process for discovering temporal patterns within a heterogenous video collection. Starting from quantized local motion descr...
    Michael C. Hughes, Erik B. Sudderth
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    TVCG
    2012
    191views Hardware» more  TVCG 2012»
    Live Speech Driven Head-and-Eye Motion Generators
    11 years 11 months ago
    Live Speech Driven Head-and-Eye Motion Generators
    Download  graphics.cs.uh.edu
    —This paper describes a fully automated framework to generate realistic head motion, eye gaze, and eyelid motion simultaneously based on live (or recorded) speech input. Its cent...
    Binh Huy Le, Xiaohan Ma, Zhigang Deng
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    ACL
    2012
    218views Computational Linguistics» more  ACL 2012»
    Building Trainable Taggers in a Web-based, UIMA-Supported NLP Workbench
    11 years 11 months ago
    Building Trainable Taggers in a Web-based, UIMA-Supported NLP Workbench
    Download  aclweb.org
    Argo is a web-based NLP and text mining workbench with a convenient graphical user interface for designing and executing processing workflows of various complexity. The workbench...
    Rafal Rak, BalaKrishna Kolluru, Sophia Ananiadou
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    AAAI
    2012
    216views Intelligent Agents» more  AAAI 2012»
    Advances in Lifted Importance Sampling
    11 years 11 months ago
    Advances in Lifted Importance Sampling
    Download  www.hlt.utdallas.edu
    We consider lifted importance sampling (LIS), a previously proposed approximate inference algorithm for statistical relational learning (SRL) models. LIS achieves substantial vari...
    Vibhav Gogate, Abhay Kumar Jha, Deepak Venugopal
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    NIPS
    1998
    154views Information Technology» more  NIPS 1998»
    Semi-Supervised Support Vector Machines
    13 years 10 months ago
    Semi-Supervised Support Vector Machines
    Download  www1.cs.columbia.edu
    We introduce a semi-supervised support vector machine (S3 VM) method. Given a training set of labeled data and a working set of unlabeled data, S3 VM constructs a support vector m...
    Kristin P. Bennett, Ayhan Demiriz
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