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Data-Dependent Path Normalization in Neural Networks

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 نشر من قبل Behnam Neyshabur
 تاريخ النشر 2015
  مجال البحث الهندسة المعلوماتية
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We propose a unified framework for neural net normalization, regularization and optimization, which includes Path-SGD and Batch-Normalization and interpolates between them across two different dimensions. Through this framework we investigate issue of invariance of the optimization, data dependence and the connection with natural gradients.


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