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Learning Narrow One-Hidden-Layer ReLU Networks
We consider the well-studied problem of learning a linear combination of $k$ ReLU activations with respect to a Gaussian distribution on inputs in $d$ dimensions.
Efficiently Learning Any One Hidden Layer ReLU Network From Queries
In this work we give the first polynomial-time algorithm for learning arbitrary one hidden layer neural networks activations provided black-box access to the network.
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