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State-of-the-art deep neural networks (DNNs) are highly effective at tackling many real-world tasks. However, their wide adoption in mission-critical contexts is hampered by two major weaknesses - their susceptibility to adversarial attacks and their
Externí odkaz:
http://arxiv.org/abs/2211.08686
Recent works have shown that the input domain of any machine learning classifier is bound to contain adversarial examples. Thus we can no longer hope to immune classifiers against adversarial examples and instead can only aim to achieve the following
Externí odkaz:
http://arxiv.org/abs/2009.11349
When Explainability Meets Adversarial Learning: Detecting Adversarial Examples using SHAP Signatures
State-of-the-art deep neural networks (DNNs) are highly effective in solving many complex real-world problems. However, these models are vulnerable to adversarial perturbation attacks, and despite the plethora of research in this domain, to this day,
Externí odkaz:
http://arxiv.org/abs/1909.03418
Akademický článek
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Autor:
Lázaro Fidel Gil Manrique, Caridad Soler Morejón, Lourdes Crespo Acebal, Miriam Villa Valdés, Lázaro Alfonso Alfonso, Isis Caridad Contreras Rojas
Publikováno v:
Revista Cubana de Cirugía, Vol 50, Iss 4, Pp 560-569 (2011)
La localización extraintestinal es una complicación temible de la amebiasis intestinal, con una elevada mortalidad, que oscila entre el 4 al 14 % de los casos diagnosticados. La forma de presentación más común es el absceso hepático amebiano, y
Externí odkaz:
https://doaj.org/article/d04aa46578684c82a5c59db2f054051c