Neural backpropagation

Results: 449



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1On derivation of stagewise second-order backpropagation by invariant imbedding for multi-stage neural-network learning Eiji Mizutani and Stuart Dreyfus Abstract— We present a simple, intuitive argument based on “inva

On derivation of stagewise second-order backpropagation by invariant imbedding for multi-stage neural-network learning Eiji Mizutani and Stuart Dreyfus Abstract— We present a simple, intuitive argument based on “inva

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Source URL: www.comp.sd.tmu.ac.jp

Language: English - Date: 2013-03-25 23:29:17
    2Backpropagation Through Time: What It Does and How to Do It PAUL J. WERBOS Backpropagation is now the most widely used tool in the field of artificial neural networks. At the core of backpropagation is a

    Backpropagation Through Time: What It Does and How to Do It PAUL J. WERBOS Backpropagation is now the most widely used tool in the field of artificial neural networks. At the core of backpropagation is a

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    Source URL: www.cs.cmu.edu

    - Date: 2013-09-09 12:05:45
      3NCSU_SAS_SAM: Deep Encoding and Reconstruction for Normalization of Noisy Text Samuel P. Leeman-Munk James C. Lester Center for Educational Informatics North Carolina State University

      NCSU_SAS_SAM: Deep Encoding and Reconstruction for Normalization of Noisy Text Samuel P. Leeman-Munk James C. Lester Center for Educational Informatics North Carolina State University

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      Source URL: noisy-text.github.io

      Language: English - Date: 2016-08-14 21:11:09
      4A Hybrid System for MorphoSyntactic Disambiguation in Bulgarian Kiril Iv. Simov and Petya N. Osenova  y

      A Hybrid System for MorphoSyntactic Disambiguation in Bulgarian Kiril Iv. Simov and Petya N. Osenova y

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      Source URL: bultreebank.org

      Language: English - Date: 2003-12-07 15:22:49
      5Kleinetal2012NCPW_revision.dvi

      Kleinetal2012NCPW_revision.dvi

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      Source URL: www.stefanfrank.info

      Language: English - Date: 2013-05-22 07:20:36
      6Optimal Gradient-Based Learning Using Importance Weights Sepp Hochreiter and Klaus Obermayer Bernstein Center for Computational Neuroscience and Technische Universit¨at Berlin

      Optimal Gradient-Based Learning Using Importance Weights Sepp Hochreiter and Klaus Obermayer Bernstein Center for Computational Neuroscience and Technische Universit¨at Berlin

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      Source URL: www.bioinf.jku.at

      Language: English - Date: 2013-01-23 02:39:45
      7Bridging Long Time Lags by Weight Guessing and \Long Short Term Memory

      Bridging Long Time Lags by Weight Guessing and \Long Short Term Memory" Sepp Hochreiter Fakultat fur Informatik, Technische Universitat Munchen

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      Source URL: www.bioinf.jku.at

      Language: English - Date: 2013-01-23 02:11:14
      8EVALUATING BENCHMARK PROBLEMS BY RANDOM GUESSING J¨ urgen Schmidhuber IDSIA Corso Elvezia 36

      EVALUATING BENCHMARK PROBLEMS BY RANDOM GUESSING J¨ urgen Schmidhuber IDSIA Corso Elvezia 36

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      Source URL: www.bioinf.jku.at

      Language: English - Date: 2015-10-02 01:16:05
      9Low-Complexity Coding and Decoding Sepp Hochreiter and Jurgen Schmidhuber Technische Universitat Munchen, 80290 Munchen, Germany and IDSIA, Corso Elvezia 36, CH-6900-Lugano, Switzerland  Abstract. We present a novel

      Low-Complexity Coding and Decoding Sepp Hochreiter and Jurgen Schmidhuber Technische Universitat Munchen, 80290 Munchen, Germany and IDSIA, Corso Elvezia 36, CH-6900-Lugano, Switzerland Abstract. We present a novel

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      Source URL: www.bioinf.jku.at

      Language: English - Date: 2013-01-23 02:15:47
      10ELU-Networks: Fast and Accurate CNN Learning on ImageNet Martin Heusel, Djork-Arné Clevert, Günter Klambauer, Andreas Mayr, Karin Schwarzbauer, Thomas Unterthiner, and Sepp Hochreiter Abstract:

      ELU-Networks: Fast and Accurate CNN Learning on ImageNet Martin Heusel, Djork-Arné Clevert, Günter Klambauer, Andreas Mayr, Karin Schwarzbauer, Thomas Unterthiner, and Sepp Hochreiter Abstract:

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      Source URL: www.bioinf.jku.at

      Language: English - Date: 2015-12-21 07:26:55