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Improved Learning of One-hidden-layer Convolutional Neural Networks with Overlaps

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 نشر من قبل Simon Du
 تاريخ النشر 2018
  مجال البحث الهندسة المعلوماتية
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We propose a new algorithm to learn a one-hidden-layer convolutional neural network where both the convolutional weights and the outputs weights are parameters to be learned. Our algorithm works for a general class of (potentially overlapping) patches, including commonly used structures for computer vision tasks. Our algorithm draws ideas from (1) isotonic regression for learning neural networks and (2) landscape analysis of non-convex matrix factorization problems. We believe these findings may inspire further development in designing provable algorithms for learning neural networks and other complex models.



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