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pro vyhledávání: '"Gawlikowski, Jakob"'
Concept Activation Vectors (CAVs) offer insights into neural network decision-making by linking human friendly concepts to the model's internal feature extraction process. However, when a new set of CAVs is discovered, they must still be translated i
Externí odkaz:
http://arxiv.org/abs/2410.17832
Autor:
Lehmann, Nils, Gawlikowski, Jakob, Stewart, Adam J., Jancauskas, Vytautas, Depeweg, Stefan, Nalisnick, Eric, Gottschling, Nina Maria
Uncertainty quantification (UQ) is an essential tool for applying deep neural networks (DNNs) to real world tasks, as it attaches a degree of confidence to DNN outputs. However, despite its benefits, UQ is often left out of the standard DNN workflow
Externí odkaz:
http://arxiv.org/abs/2410.03390
Publikováno v:
Lecture Notes in Computer Science, vol. 14949 (2024) 207-222
The detection of abnormal or critical system states is essential in condition monitoring. While much attention is given to promptly identifying anomalies, a retrospective analysis of these anomalies can significantly enhance our comprehension of the
Externí odkaz:
http://arxiv.org/abs/2406.09825
Publikováno v:
AAAI, vol. 37, no. 8, pp. 9134-9142, Jun. 2023
There is a significant need for principled uncertainty reasoning in machine learning systems as they are increasingly deployed in safety-critical domains. A new approach with uncertainty-aware regression-based neural networks (NNs), based on learning
Externí odkaz:
http://arxiv.org/abs/2205.10060
Autor:
Gawlikowski, Jakob, Tassi, Cedrique Rovile Njieutcheu, Ali, Mohsin, Lee, Jongseok, Humt, Matthias, Feng, Jianxiang, Kruspe, Anna, Triebel, Rudolph, Jung, Peter, Roscher, Ribana, Shahzad, Muhammad, Yang, Wen, Bamler, Richard, Zhu, Xiao Xiang
Due to their increasing spread, confidence in neural network predictions became more and more important. However, basic neural networks do not deliver certainty estimates or suffer from over or under confidence. Many researchers have been working on
Externí odkaz:
http://arxiv.org/abs/2107.03342
Understanding and representing traffic patterns are key to detecting anomalous trajectories in the transportation domain. However, some trajectories can exhibit heterogeneous maneuvering characteristics despite confining to normal patterns. Thus, we
Externí odkaz:
http://arxiv.org/abs/2107.01557
In satellite image analysis, distributional mismatch between the training and test data may arise due to several reasons, including unseen classes in the test data and differences in the geographic area. Deep learning based models may behave in unexp
Externí odkaz:
http://arxiv.org/abs/2104.05442
Publikováno v:
In Informatics in Medicine Unlocked 2024 48
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