Despite being responsible for state-of-the-art results in several computer vision and natural
language processing tasks, neural networks have faced harsh criticism due to some of their …

Machine learning has been heavily researched and widely used in many disciplines.
However, achieving high accuracy requires a large amount of data that is sometimes …

With the advent of deep learning, many dense prediction tasks, ie, tasks that produce pixel-
level predictions, have seen significant performance improvements. The typical approach is …

Many computer vision applications require solving multiple tasks in real-time. A neural
network can be trained to solve multiple tasks simultaneously using multi-task learning. This …

Streamflow and flood forecasting remains one of the long-standing challenges in hydrology.
Traditional physically based models are hampered by sparse parameters and complex …

Over recent years, deep learning-based computer vision systems have been applied to
images at an ever-increasing pace, oftentimes representing the only type of consumption for …

The automated analysis of eye fundus images is crucial towards facilitating the screening
and early diagnosis of glaucoma. Nowadays, there are two common alternatives for the …

With the development of hardware devices, infrared technology has become an important
detection method in criminal investigation, military and other fields. Infrared technology can …

Despite the recent advances in multi-task learning of dense prediction problems, most
methods rely on expensive labelled datasets. In this paper, we present a label efficient …

In recent years, research in applying optimization approaches in the automatic design of
deep neural networks has become increasingly popular. Although various approaches have …