Despite its astounding success in learning deeper multi-dimensional data, the performance
of deep learning declines on new unseen tasks mainly due to its focus on same-distribution …

Proteomics, the study of all the proteins in biological systems, is becoming a data‐rich
science. Protein sequences and structures are comprehensively catalogued in online …

Understanding broadcast videos is a challenging task in computer vision, as it requires
generic reasoning capabilities to appreciate the content offered by the video editing. In this …

The problem of concept drift has gained a lot of attention in recent years. This aspect is key
in many domains exhibiting non-stationary as well as cyclic patterns and structural breaks …

Progress in deep learning, more specifically in using convolutional neural networks (CNNs)
for the creation of classification models, has been tremendous in recent years. Within …

Abstract Machine learning has been widely adopted for fault detection and diagnosis (FDD)
in heating, ventilation and air conditioning (HVAC) systems over the past decade due to the …

Applying computational statistics or machine learning methods to data is a key component of
many scientific studies, in any field, but alone might not be sufficient to generate robust and …

Zero-day malware is malware that has never been seen before or is so new that no anti-
malware software can catch it. This novelty and the lack of existing mitigation strategies …

While physical changes have been empirically recognised as a fundamental component of
neighbourhood change, data and modelling constraints have limited the quantification of …

Abstract The Exa. TrkX project has applied geometric learning concepts such as metric
learning and graph neural networks to HEP particle tracking. Exa. TrkX's tracking pipeline …