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Rong Ge : Learning Two-Layer Neural Networks with Symmetric Inputs (Feb 27, 2019 11:55 AM)

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

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