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

Simon S. Du; Surbhi Goel

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arXiv · 1805.07798

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

Simon S. Du·Surbhi Goel

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Abstract

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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cs.LG cs.AI cs.CV cs.DS stat.ML

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BibTeX RIS

Simon S. Du, Surbhi Goel. 2018-05-20. Improved Learning of One-hidden-layer Convolutional Neural Networks with Overlaps. https://arxiv.org/abs/1805.07798

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