Zobrazeno 1 - 10
of 44
pro vyhledávání: '"Lebacher, Michael"'
Autor:
Decker, Thomas, Koebler, Alexander, Lebacher, Michael, Thon, Ingo, Tresp, Volker, Buettner, Florian
Monitoring and maintaining machine learning models are among the most critical challenges in translating recent advances in the field into real-world applications. However, current monitoring methods lack the capability of provide actionable insights
Externí odkaz:
http://arxiv.org/abs/2408.13648
Deep Learning has already been successfully applied to analyze industrial sensor data in a variety of relevant use cases. However, the opaque nature of many well-performing methods poses a major obstacle for real-world deployment. Explainable AI (XAI
Externí odkaz:
http://arxiv.org/abs/2310.12967
Concept-based explanation methods, such as Concept Activation Vectors, are potent means to quantify how abstract or high-level characteristics of input data influence the predictions of complex deep neural networks. However, applying them to industri
Externí odkaz:
http://arxiv.org/abs/2310.11450
Modern AI techniques open up ever-increasing possibilities for autonomous vehicles, but how to appropriately verify the reliability of such systems remains unclear. A common approach is to conduct safety validation based on a predefined Operational D
Externí odkaz:
http://arxiv.org/abs/2310.10635
Autor:
Decker, Thomas, Gross, Ralf, Koebler, Alexander, Lebacher, Michael, Schnitzer, Ronald, Weber, Stefan H.
In this paper, we investigate the practical relevance of explainable artificial intelligence (XAI) with a special focus on the producing industries and relate them to the current state of academic XAI research. Our findings are based on an extensive
Externí odkaz:
http://arxiv.org/abs/2310.07882
To capture the systemic complexity of international financial systems, network data is an important prerequisite. However, dyadic data is often not available, raising the need for methods that allow for reconstructing networks based on limited inform
Externí odkaz:
http://arxiv.org/abs/1909.01274
Given the growing number of available tools for modeling dynamic networks, the choice of a suitable model becomes central. The goal of this survey is to provide an overview of tie-oriented dynamic network models. The survey is focused on introducing
Externí odkaz:
http://arxiv.org/abs/1905.10351
Autor:
Lebacher, Michael, Kauermann, Göran
Network (or matrix) reconstruction is a general problem which occurs if the margins of a matrix are given and the matrix entries need to be predicted. In this paper we show that the predictions obtained from the iterative proportional fitting procedu
Externí odkaz:
http://arxiv.org/abs/1903.11886
In this paper we use a censored regression model to investigate data on the international trade of small arms and ammunition (SAA) provided by the Norwegian Initiative on Small Arms Transfers (NISAT). Taking a network based view on the transfers, we
Externí odkaz:
http://arxiv.org/abs/1902.09292
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.