Artificial Intelligence-based Uncertainty Quantification (AUQ)

IEEE SSCI 2020 will run as a virtual conference.

The field of artificial intelligence-based uncertainty quantification has gained an overwhelming attention among researchers in recent years resulting in an arsenal of different methods. It aims to improve the reliability of computational intelligence models by enabling them to know when they do not know. Uncertainty quantification has immediate and key benefits in critical applications such as autonomous systems, cybersecurity, health, bioinformatics, finance, and transportation and energy networks. There are currently fundamental and practical issues with frameworks utilized for uncertainty quantification using artificial intelligence methods in particular deep neural networks and fuzzy logic systems. Also, further research is required for automated the integration of quantified uncertainties in the process of decision making, scheduling, and optimisation.

The goal is to provide an in-depth discussion of the latest academic and industrial research findings of artificial intelligence-based uncertainty quantification. The session will get together prominent and upcoming scientist from around the world who are conducting research on neural network-based uncertainty quantification using various approaches. Topics of interest include, but are not limited to:

Uncertainty quantification for different neural network types (feedforward, CNN, recurrent, LSTM, encoders, etc)
Uncertainty quantification using fuzzy logic systems (type 1 and 2, neuro-fuzzy, etc)
Bayesian methods for uncertainty quantification
Ensemble-based uncertainty quantification
Direct methods for uncertainty quantification
Uncertainty-aware decision making (classification and regression)
Applications for data-driven modelling and forecasting
Applications for classification and regression problems