Orateur
Laurens Sluijterman
(Radboud University)
Description
In this talk, we delve into the complexities of uncertainty quantification for neural networks. Model predictions inherently come with uncertainties that arise from several factors: stochastic outcomes, the randomness of training data samples, and the inherent variability of the training process itself. Through the lens of a regression problem, we will unpack these factors and provide a pragmatic framework to understand and quantify uncertainty effectively. Furthermore, we will discuss various considerations and pitfalls associated with using popular approaches such as Monte Carlo dropout and Deep Ensembles.
Auteur principal
Laurens Sluijterman
(Radboud University)
