What has happened in machine learning lately, and what does it mean for the future of
medical image analysis? Machine learning has witnessed a tremendous amount of attention …

We develop BatchBALD, a tractable approximation to the mutual information between a
batch of points and model parameters, which we use as an acquisition function to select …

Detecting out-of-distribution examples is important for safety-critical machine learning
applications such as detecting novel biological phenomena and self-driving cars. However …

Deep learning is a fast-growing machine learning approach to perceive and understand
large amounts of data. In this paper, general information about the deep learning approach …

Deep learning (DL)-based semantic segmentation methods have been providing state-of-
the-art performance in the past few years. More specifically, these techniques have been …

Timely monitoring of pavement cracks is essential for successful maintenance of road
infrastructure. Accurate information concerning crack location and severity enables proactive …

Assessing reliably the confidence of a deep neural net and predicting its failures is of
primary importance for the practical deployment of these models. In this paper, we propose a …

With advanced image journaling tools, one can easily alter the semantic meaning of an
image by exploiting certain manipulation techniques such as copy clone, object splicing …

Physics-informed neural networks (PINNs) have recently emerged as an alternative way of
numerically solving partial differential equations (PDEs) without the need of building …

In this paper, we propose a novel Pattern-Affinitive Propagation (PAP) framework to jointly
predict depth, surface normal and semantic segmentation. The motivation behind it comes …