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Understanding Convolutional Neural Networks with AMathematical Jay KuoMing-Hsieh Department of Electrical EngineeringUniversity of Southern California, Los Angeles, CA 90089-2564, USAA bstractThis work attempts to address two fundamental questions about thestructure of the Convolutional Neural Networks (CNN): 1) why a nonlinear ac-tivation function is essential at the filter output of all intermediate layers? 2)what is the advantage of the two-layer cascade system over the one-layer sys-tem? A mathematical model called the REctified-COrrelations on a Sphere (RECOS) is proposed to answer these two questions. After the CNN train-ing process, the converged filter weights define a set of anchor vectors in theRECOS model.
Understanding Convolutional Neural Networks with A Mathematical Model C.-C. Jay Kuo Ming-Hsieh Department of Electrical Engineering University of …
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