Noticeable uncertainties affect the modelling procedure: they can be due to measurement errors on the training data, to the finite number of samples in the data ...

Training a feedforward neural network leads to a model affected by uncertainty. A measure of this uncertainty gives an important indication of neural ...

Article "Finite sample size and neural model uncertainty." Detailed information of the J-GLOBAL is an information service managed by the Japan Science and ...

This article will focus on building predictive models that surrogate more resource-intensive CAE models and uncertainty quantification in deep learning for ...

Mar 15, 2024 · Uncertainty is a key feature of any machine learning model and is particularly important in neural networks, which tend to be overconfident.

Sep 25, 2019 · Uncertainty means working with imperfect or incomplete information. Uncertainty is fundamental to the field of machine learning, yet it is one of the aspects ...

Mar 18, 2024 · Here, we review the topic of predictive uncertainty estimation with machine learning algorithms, as well as the related metrics (consistent scoring functions ...

Duration: 33:29
Posted: Aug 16, 2023

Probabilistic ML, ensemble learning, and optimization provide a foundation. ○ The best methods advance two dimensions: combining multiple neural network.

Finally, the importance of the contribution of finite trainers to the uncertainties argues for some form of bootstrap analysis to sample that uncertainty.