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pro vyhledávání: '"Shen, Haojing"'
It is necessary to improve the performance of some special classes or to particularly protect them from attacks in adversarial learning. This paper proposes a framework combining cost-sensitive classification and adversarial learning together to trai
Externí odkaz:
http://arxiv.org/abs/2101.12372
In this paper, we propose a defence strategy to improve adversarial robustness by incorporating hidden layer representation. The key of this defence strategy aims to compress or filter input information including adversarial perturbation. And this de
Externí odkaz:
http://arxiv.org/abs/2011.14045
This paper shows a Min-Max property existing in the connection weights of the convolutional layers in a neural network structure, i.e., the LeNet. Specifically, the Min-Max property means that, during the back propagation-based training for LeNet, th
Externí odkaz:
http://arxiv.org/abs/2011.13719
In many signal processing applications of Kalman filter (KF) and its variants and extensions, accurate estimation of extreme states is often of great importance. When the observations used are uncertain, however, KF suffers from conditional bias (CB)
Externí odkaz:
http://arxiv.org/abs/1908.00482
Akademický článek
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Publikováno v:
In Computers and Geosciences September 2022 166
Publikováno v:
In Neural Networks June 2022 150:1-11
Publikováno v:
In Journal of Hydrology February 2022 605
Autor:
Kim, Sunghee, Shen, Haojing, Noh, Seongjin, Seo, Dong-Jun, Welles, Edwin, Pelgrim, Erik, Weerts, Albrecht, Lyons, Eric, Philips, Brenda
Publikováno v:
In Journal of Hydrology July 2021 598
Publikováno v:
In Journal of Hydrology August 2019 575:596-611