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of 25 239
pro vyhledávání: '"discovery methods"'
Machine learning models have achieved high overall accuracy in medical image analysis. However, performance disparities on specific patient groups pose challenges to their clinical utility, safety, and fairness. This can affect known patient groups -
Externí odkaz:
http://arxiv.org/abs/2406.12142
Autor:
Alkhatabi, Hind A. a, ⁎⁎, Pushparaj, Peter Natesan b, ⁎
Publikováno v:
In Heliyon 15 January 2025 11(1)
Autor:
Lorbeer, Boris, Mohsen, Mustafa
Nowadays, the need for causal discovery is ubiquitous. A better understanding of not just the stochastic dependencies between parts of a system, but also the actual cause-effect relations, is essential for all parts of science. Thus, the need for rel
Externí odkaz:
http://arxiv.org/abs/2401.13009
The practical utility of causality in decision-making is widespread and brought about by the intertwining of causal discovery and causal inference. Nevertheless, a notable gap exists in the evaluation of causal discovery methods, where insufficient e
Externí odkaz:
http://arxiv.org/abs/2307.04988
We study the problem of determining the emergent behaviors that are possible given a functionally heterogeneous swarm of robots with limited capabilities. Prior work has considered behavior search for homogeneous swarms and proposed the use of novelt
Externí odkaz:
http://arxiv.org/abs/2310.16941
Autor:
Wang, Kai
Publikováno v:
Data Technologies and Applications, 2024, Vol. 58, Issue 4, pp. 632-651.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/DTA-09-2023-0570
Akademický článek
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The ability to understand causality from data is one of the major milestones of human-level intelligence. Causal Discovery (CD) algorithms can identify the cause-effect relationships among the variables of a system from related observational data wit
Externí odkaz:
http://arxiv.org/abs/2303.15027
Causal relationships are commonly examined in manufacturing processes to support faults investigations, perform interventions, and make strategic decisions. Industry 4.0 has made available an increasing amount of data that enable data-driven Causal D
Externí odkaz:
http://arxiv.org/abs/2208.01529