Modelling the influence of data structure on learning in neural networks: the hidden manifold model

25 Sep 2019Sebastian GoldtMarc MézardFlorent KrzakalaLenka Zdeborová

Understanding the reasons for the success of deep neural networks trained using stochastic gradient-based methods is a key open problem for the nascent theory of deep learning. The types of data where these networks are most successful, such as images or sequences of speech, are characterised by intricate correlations... (read more)

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