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Reconstruction of training samples from loss functions
[article]
2018
arXiv
pre-print
This paper presents a new mathematical framework to analyze the loss functions of deep neural networks with ReLU functions. Furthermore, as as application of this theory, we prove that the loss functions can reconstruct the inputs of the training samples up to scalar multiplication (as vectors) and can provide the number of layers and nodes of the deep neural network. Namely, if we have all input and output of a loss function (or equivalently all possible learning process), for all input of
arXiv:1805.07337v1
fatcat:xd4yowpcujb5no5qytturcuzmi