Uncertainty estimation and ensembling methods go hand-in-hand. Uncertainty estimation is
one of the main benchmarks for assessment of ensembling performance. At the same time …

Deep learning (DL) methods have been widely applied to intelligent fault diagnosis of
industrial processes and achieved state-of-the-art performance. However, fault diagnosis …

In this article, a multi-estimator based computationally efficient algorithm is developed for
autonomous search in an unknown environment with an unknown source. Different from the …

Multiple instance learning (MIL) is a popular weakly-supervised learning model on the
whole slide image (WSI) for AI-assisted pathology diagnosis. The recent advance in …

Visual data contains rich information about the operating environment of an intelligent
robotic system. Extracting this information allows intelligent systems to reason and decide …

The quest for optimal operation in environments with unknowns and uncertainties is highly
desirable but critically challenging across numerous fields. This paper develops a dual …

Nested dropout is a variant of dropout operation that is able to order network parameters or
features based on the pre-defined importance during training. It has been explored for: I …

Pool-based Active Learning (AL) has achieved great success in minimizing labeling cost by
sequentially selecting informative unlabeled samples from a large unlabeled data pool and …

Bayesian models have many desirable properties, most notable is their ability to generalize
from limited data and to properly estimate the uncertainty in their predictions. However …

In safety-critical applications such as medical imaging and autonomous driving, where
decisions have profound implications for patient health and road safety, it is imperative to …