Modeling data uncertainty is important for noisy images, but seldom explored for face
recognition. The pioneer work, PFE, considers uncertainty by modeling each face image …

A wind speed forecasting technique, using deep learning architectures based on long short-
term memory (LSTM) model and bidirectional long short-term memory (BiLSTM) model is …

This paper proposes a fast and scalable method for uncertainty quantification of machine
learning models' predictions. First, we show the principled way to measure the uncertainty of …

Among the several emerging dimensioning, control and deployment of future
communication network paradigms stands out the human-centric characteristic that creates …

Safety standards in domains like automotive and avionics seek for deterministic execution
(lack of jittery behavior) as a stepping stone to build a certification argument on the correct …

Despite the major progress of deep models as learning machines, uncertainty estimation
remains a major challenge. Existing solutions rely on modified loss functions or architectural …

With the commoditization of machine learning, more and more off-the-shelf models are
available as part of code libraries or cloud services. Typically, data scientists and other users …

This paper proposes a fast and scalable method for uncertainty quantification of machine
learning models' predictions. First, we show the principled way to measure the uncertainty of …

Recent developments have shown that modeling in the spectral domain improves the
accuracy in time series forecasting. However, state-of-the-art neural spectral forecasters do …

The recent popularity of using deep learning models for the forecasting of time series calls
for methods to not only predict the target but also measure the uncertainty of the prediction …