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Trace norm regularization and faster inference for embedded speech recognition RNNs

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 نشر من قبل Markus Kliegl
 تاريخ النشر 2017
  مجال البحث الهندسة المعلوماتية
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We propose and evaluate new techniques for compressing and speeding up dense matrix multiplications as found in the fully connected and recurrent layers of neural networks for embedded large vocabulary continuous speech recognition (LVCSR). For compression, we introduce and study a trace norm regularization technique for training low rank factor



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