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    NIPS
    1996
    135views Information Technology» more  NIPS 1996»
    Transformation Invariance in Pattern Recognition-Tangent Distance and Tangent Propagation
    14 years 26 days ago
    Transformation Invariance in Pattern Recognition-Tangent Distance and Tangent Propagation
    Download  home.mit.bme.hu
    In pattern recognition, statistical modeling, or regression, the amount of data is a critical factor affecting the performance. If the amount of data and computational resources ar...
    Patrice Simard, Yann LeCun, John S. Denker, Bernar...
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    NIPS
    1996
    100views Information Technology» more  NIPS 1996»
    Monotonicity Hints
    14 years 26 days ago
    Monotonicity Hints
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    : Neural networks are competitive tools for classification problems. In this context, a hint is any piece of prior side information about the classification. Common examples are mo...
    Joseph Sill, Yaser S. Abu-Mostafa
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    NIPS
    1996
    112views Information Technology» more  NIPS 1996»
    Exploiting Model Uncertainty Estimates for Safe Dynamic Control Learning
    14 years 26 days ago
    Exploiting Model Uncertainty Estimates for Safe Dynamic Control Learning
    Download  www.ri.cmu.edu
    Model learning combined with dynamic programming has been shown to be e ective for learning control of continuous state dynamic systems. The simplest method assumes the learned mod...
    Jeff G. Schneider
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    NIPS
    1996
    112views Information Technology» more  NIPS 1996»
    Learning from Demonstration
    14 years 26 days ago
    Learning from Demonstration
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    Stefan Schaal
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    NIPS
    1996
    98views Information Technology» more  NIPS 1996»
    Second-order Learning Algorithm with Squared Penalty Term
    14 years 26 days ago
    Second-order Learning Algorithm with Squared Penalty Term
    Download  www.kecl.ntt.co.jp
    This paper compares three penalty terms with respect to the efficiency of supervised learning, by using first- and second-order learning algorithms. Our experiments showed that fo...
    Kazumi Saito, Ryohei Nakano
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    NIPS
    1996
    128views Information Technology» more  NIPS 1996»
    Neural Models for Part-Whole Hierarchies
    14 years 26 days ago
    Neural Models for Part-Whole Hierarchies
    Download  riesenhuberlab.neuro.georgetown.edu
    We present a connectionist method for representing images that explicitlyaddresses their hierarchicalnature. It blends data fromneuroscience about whole-object viewpoint sensitive...
    Maximilian Riesenhuber, Peter Dayan
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    NIPS
    1996
    117views Information Technology» more  NIPS 1996»
    Early Stopping-But When?
    14 years 26 days ago
    Early Stopping-But When?
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    Abstract. Validation can be used to detect when over tting starts during supervised training of a neural network; training is then stopped before convergence to avoid the over ttin...
    Lutz Prechelt
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    NIPS
    1996
    134views Information Technology» more  NIPS 1996»
    Why did TD-Gammon Work?
    14 years 26 days ago
    Why did TD-Gammon Work?
    Download  www.cse.unsw.edu.au
    Although TD-Gammon is one of the major successes in machine learning, it has not led to similar impressive breakthroughs in temporal difference learning for other applications or ...
    Jordan B. Pollack, Alan D. Blair
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    NIPS
    1996
    162views Information Technology» more  NIPS 1996»
    Maximum Likelihood Blind Source Separation: A Context-Sensitive Generalization of ICA
    14 years 26 days ago
    Maximum Likelihood Blind Source Separation: A Context-Sensitive Generalization of ICA
    Download  bme.ccny.cuny.edu
    In the square linear blind source separation problem, one must nd a linear unmixing operator which can detangle the result xi(t) of mixing n unknown independent sources si(t) thro...
    Barak A. Pearlmutter, Lucas C. Parra
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    NIPS
    1996
    124views Information Technology» more  NIPS 1996»
    Are Hopfield Networks Faster than Conventional Computers?
    14 years 26 days ago
    Are Hopfield Networks Faster than Conventional Computers?
    Download  www.eng.unt.edu
    It is shown that conventional computers can be exponentially faster than planar Hopfield networks: although there are planar Hopfield networks that take exponential time to conver...
    Ian Parberry, Hung-Li Tseng
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