Uncertainty estimation with deep learning for rainfall–runoff modeling

Bayesian Deep Learning | Uncertainty in Deep Learning

Uncertainty in Deep Learning

Deep learning has attracted tremendous attention from researchers in various fields of information engineering such as AI, computer vision, and language processing [Kalchbrenner and Blunsom, 2013; Krizhevsky et al., 2012; Mnih et al., 2013], but also from more traditional sciences such as physics, biology, and manufacturing [Anjos et al., 2015; Baldi et al., 2014; Bergmann et al., 2014]. Neural...

Uncertainty Quality | Uncertainty in Deep Learning

In this chapter we assess the techniques developed in the previous chapters, concentrating on questions such as what our model uncertainty looks like. We experiment with different model architectures and approximating distributions, and use various regression and classification settings. Assessing the models’ confidence quantitatively we can see how much we sacrifice in our attempt at deriving ...

Deep Insights | Uncertainty in Deep Learning

Until now we have mostly studied the proposed approximate inference techniques empirically. In this chapter we turn to a more theoretical analysis, and concentrate mostly on the case of dropout, as it seems to be the most widely used among the various stochastic regularisation techniques. We begin by suggesting practical considerations for getting good uncertainty estimates, followed by a revie...

Applications | Uncertainty in Deep Learning

In previous chapters we linked stochastic regularisation techniques (SRTs) to approximate inference in Bayesian neural networks (NNs), and studied the resulting model uncertainty for popular SRTs such as dropout. We have yet to give any real-world applications stemming from this link though, leaving it somewhat in the realms of “theoretical work”. But a theory is worth very little if we can’t u...