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2025年8月17日 (日) 11:42時点におけるClaireWhitington (トーク | 投稿記録)による版 (ページの作成:「<br>Neural networks have been widely utilized for performing duties in essential domains, such as, for example, the medical domain; their robustness is, therefore, vital to be guaranteed. On this paper, we propose a robustness definition for neural networks used for regression, by tackling some of the issues of present robustness definitions. To begin with, by following latest works performed for classification problems, we propose to outline the robustness of netwo…」)
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Neural networks have been widely utilized for performing duties in essential domains, such as, for example, the medical domain; their robustness is, therefore, vital to be guaranteed. On this paper, we propose a robustness definition for neural networks used for regression, by tackling some of the issues of present robustness definitions. To begin with, by following latest works performed for classification problems, we propose to outline the robustness of networks used for regression w.r.t. Since different alteration levels should not at all times equally probable, the robustness definition is parameterized with the likelihood distribution of the alterations. The error carried out by one of these networks is quantifiable as the difference between the estimated worth and the expected worth; since not all the errors are equally vital, the robustness definition is also parameterized with a "tolerance" function that specifies how the error Blood Vitals is tolerated. The current work has been motivated by the collaboration with the industrial companion that has carried out a medical sensor employing a Multilayer Perceptron for the estimation of the blood oxygen strain. After having computed the robustness for the case examine, now we have successfully applied three techniques to improve the community robustness: knowledge augmentation with recombined knowledge, data augmentation with altered data, and incremental learning. All of the techniques have proved to contribute to growing the robustness, although in alternative ways.



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