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A Survey of Uncertainty in Deep Neural Networks. (arXiv:2107.03342v3 [cs.LG] UPDATED)
Jan. 20, 2022, 2:11 a.m. | Jakob Gawlikowski, Cedrique Rovile Njieutcheu Tassi, Mohsin Ali, Jongseok Lee, Matthias Humt, Jianxiang Feng, Anna Kruspe, Rudolph Triebel, Peter Jung
cs.LG updates on arXiv.org arxiv.org
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 understanding and quantifying uncertainty in a neural
network's prediction. As a result, different types and sources of uncertainty
have been identified and a variety of approaches to measure and quantify
uncertainty in neural networks have been proposed. This work gives a
comprehensive overview …
More from arxiv.org / cs.LG updates on arXiv.org
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