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Analysis of function approximation and stability of general DNNs in directed acyclic graphs using un-rectifying analysis
In this paper, we made a number of basic assumptions pertaining to activation functions, non-linear transformations, and DNN architectures in order to use the un-rectifying method to analyze DNNs via directed acyclic graphs (DAGs).
Learning DNN networks using un-rectifying ReLU with compressed sensing application
However, we demonstrate that the optimal solution to a combinatorial optimization problem can be preserved by relaxing the discrete domains of activation variables to closed intervals.
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