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Learning Two-Layer Neural Networks with Symmetric Inputs

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Wednesday, February 27, 2019
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12:00 pm - 1:00 pm
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Rong Ge (Duke University, Computer Science)
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Applied Math And Analysis Seminar

Deep learning has been extremely successful in practice. However, existing guarantees for learning neural networks are limited even when the network has only two layers - they require strong assumptions either on the input distribution or on the norm of the weight vectors. In this talk we give a new algorithm that is guaranteed to learn a two-layer neural network under much milder assumptions on the input distribution. Our algorithms works whenever the input distribution is symmetric - which means two inputs x and ¿x have the same probability.

Based on joint work with Rohith Kuditipudi, Zhize Li and Xiang Wang