Learnability of Learned Neural Networks

ICLR 2018 Rahul Anand SharmaNavin GoyalMonojit ChoudhuryPraneeth Netrapalli

This paper explores the simplicity of learned neural networks under various settings: learned on real vs random data, varying size/architecture and using large minibatch size vs small minibatch size. The notion of simplicity used here is that of learnability i.e., how accurately can the prediction function of a neural network be learned from labeled samples from it... (read more)

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