Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Manchingal, Shireen Kudukkil"'
Autor:
Wang, Kaizheng, Shariatmadar, Keivan, Manchingal, Shireen Kudukkil, Cuzzolin, Fabio, Moens, David, Hallez, Hans
Uncertainty estimation is increasingly attractive for improving the reliability of neural networks. In this work, we present novel credal-set interval neural networks (CreINNs) designed for classification tasks. CreINNs preserve the traditional inter
Externí odkaz:
http://arxiv.org/abs/2401.05043
Autor:
Manchingal, Shireen Kudukkil, Mubashar, Muhammad, Wang, Kaizheng, Shariatmadar, Keivan, Cuzzolin, Fabio
Machine learning is increasingly deployed in safety-critical domains where robustness against adversarial attacks is crucial and erroneous predictions could lead to potentially catastrophic consequences. This highlights the need for learning systems
Externí odkaz:
http://arxiv.org/abs/2307.05772
The belief function approach to uncertainty quantification as proposed in the Demspter-Shafer theory of evidence is established upon the general mathematical models for set-valued observations, called random sets. Set-valued predictions are the most
Externí odkaz:
http://arxiv.org/abs/2206.07609
Autor:
Manchingal, Shireen Kudukkil, Mubashar, Muhammad, Wang, Kaizheng, Shariatmadar, Keivan, Cuzzolin, Fabio
Machine learning is increasingly deployed in safety-critical domains where robustness against adversarial attacks is crucial and erroneous predictions could lead to potentially catastrophic consequences. This highlights the need for learning systems
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dd6b596c733394560e30348d124d2d5b
http://arxiv.org/abs/2307.05772
http://arxiv.org/abs/2307.05772