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arXiv Machine Learning By H. Martin Gillis, Thomas Trappenberg

Uncertainty quantification for trustworthy deep learning: Methods and measures

Read the original on arXiv Machine Learning →

arXiv:2607. 28248v1 Announce Type: cross Abstract: The deployment of deep neural networks in safety-critical domains demands reliable estimates of predictive confidence, yet conventional architectures lack principled uncertainty quantification.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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