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    NIPS
    2008
    285views Information Technology» more  NIPS 2008»
    Natural Image Denoising with Convolutional Networks
    13 years 9 months ago
    Natural Image Denoising with Convolutional Networks
    Download  hebb.mit.edu
    We present an approach to low-level vision that combines two main ideas: the use of convolutional networks as an image processing architecture and an unsupervised learning procedu...
    Viren Jain, H. Sebastian Seung
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    AAAI
    2006
    150views Intelligent Agents» more  AAAI 2006»
    LOCATE Intelligent Systems Demonstration: Adapting Help to the Cognitive Styles of Users
    13 years 9 months ago
    LOCATE Intelligent Systems Demonstration: Adapting Help to the Cognitive Styles of Users
    Download  www.aaai.org
    LOCATE is workspace layout design software that also serves as a testbed for developing and refining principles of adaptive aiding. This demonstration illustrates LOCATE's ab...
    Jack L. Edwards, Greg Scott
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    ACL
    2003
    134views Computational Linguistics» more  ACL 2003»
    Generalized Algorithms for Constructing Statistical Language Models
    13 years 9 months ago
    Generalized Algorithms for Constructing Statistical Language Models
    Download  cs.nyu.edu
    Recent text and speech processing applications such as speech mining raise new and more general problems related to the construction of language models. We present and describe in...
    Cyril Allauzen, Mehryar Mohri, Brian Roark
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    DAGSTUHL
    2001
    187views Software Engineering» more  DAGSTUHL 2001»
    An Overview of the GXL Graph Exchange Language
    13 years 9 months ago
    An Overview of the GXL Graph Exchange Language
    Download  www.gupro.de
    GXL (Graph eXchange Language) is designed to be a standard exchange format for graph-based tools. GXL is defined as an XML sublanguage, which offers support for exchanging instanc...
    Andreas Winter, Bernt Kullbach, Volker Riediger
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    SDM
    2003
    SIAM
    184views Data Mining» more  SDM 2003»
    Finding Clusters of Different Sizes, Shapes, and Densities in Noisy, High Dimensional Data
    13 years 9 months ago
    Finding Clusters of Different Sizes, Shapes, and Densities in Noisy, High Dimensional Data
    Download  www-users.cs.umn.edu
    The problem of finding clusters in data is challenging when clusters are of widely differing sizes, densities and shapes, and when the data contains large amounts of noise and out...
    Levent Ertöz, Michael Steinbach, Vipin Kumar
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