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Membership-Mappings for Practical Secure Distributed Deep Learning
This study leverages the data representation capability of fuzzy based membership-mappings for practical secure distributed deep learning using fully homomorphic encryption.
Revisiting the dynamics of Bose-Einstein condensates in a double well by deep learning with a hybrid network
Deep learning, accounting for the use of an elaborate neural network, has recently been developed as an efficient and powerful tool to solve diverse problems in physics and other sciences.
THORS: An Efficient Approach for Making Classifiers Cost-sensitive
In this paper, we propose an effective THresholding method based on ORder Statistic, called THORS, to convert an arbitrary scoring-type classifier, which can induce a continuous cumulative distribution function of the score, into a cost-sensitive one.
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