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In this paper we study the learnability of deep random networks from both theoretical and practical points of view.
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Beating the perils of non-convexity: Guaranteed training of neural networks using tensor methods
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Learning halfspaces and neural networks with random initialization
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Efficient approaches for escaping higher order saddle points in non-convex optimization
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Complexity theoretic limitations on learning halfspaces
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A general characterization of the statistical query complexity
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Eigenvalue decay implies polynomial-time learnability for neural networks
S. Goel and A. R. Klivans · 2017
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Convergence analysis of two-layer neural networks with ReLU activation
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Learning non-overlapping convolutional neural networks with multiple kernels
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Recovery guarantees for one-hidden-layer neural networks
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V. Feldman · 2017
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Reliably learning the ReLU in polynomial time
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S. S. Du, J. D. Lee, Y. Tian, A. Singh, and B. Póczos · 2018
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Convergence results for neural networks via electrodynamics
R. Panigrahy, A. Rahimi, S. Sachdeva, and Q. Zhang · 2018
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