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We state concentration inequalities for the output of the hidden layers of a stochastic deep neural network (SDNN), as well as for the output of the whole SDNN. These results… Click to show full abstract
We state concentration inequalities for the output of the hidden layers of a stochastic deep neural network (SDNN), as well as for the output of the whole SDNN. These results allow us to introduce an expected classifier (EC), and to give probabilistic upper bound for the classification error of the EC. We also state the optimal number of layers for the SDNN via an optimal stopping procedure. We apply our analysis to a stochastic version of a feedforward neural network with ReLU activation function.
Keywords:
concentration inequalities;
optimal number;
number layers;
deep neural;
layers stochastic;
stochastic deep
Journal Title: IEEE Access
Year Published: 2022
Link to full text (if available)
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Journal Title: IEEE Access
Year Published: 2022
Link to full text (if available)
Share on Social Media: Sign Up to like & get
recommendations!