Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2020; 29th International C Igor Farkaš,Paolo Masulli,Stefan Wermter Conference proc

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,Berührungslos/optische Messverfahren,unov function of the network model, the deterministic equations of motion (phase-space flow) of the network, and the form of the diffusion coefficients appearing in the nonlinear Fokker-Planck equations. This, in turn, leads to an .-theorem involving a free energy-like functional related to the . en

Glaci冰

A Lightweight Fully Convolutional Neural Network of High Accuracy Surface Defect Detection improving accuracy. However, it is difficult to apply in real situation, because of huge number of parameters and the strict hardware requirements. In this paper, a lightweight fully convolutional neural network, named LFCSDD, is proposed. The parameters of our model are 11x fewer than baselines at

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Detecting Uncertain BNN Outputs on FPGA Using Monte Carlo Dropout Samplingep Neural Network (DNN). However, because it takes a long time to sample DNN’s output for calculating its distribution, it is difficult to apply it to edge computing where resources are limited. Thus, this research proposes a method of reducing a sampling time required for MC Dropout in edge computi

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