Machine learning (ML), as a branch of artificial intelligence, acquires the potential and
meaningful rules from the mass of data via diverse algorithms. Owing to all research of …

Many convolutional neural networks (CNNs) for single image deblurring employ a U-Net
structure to estimate latent sharp images. Having long been proven to be effective in image …

Convolutional networks are ubiquitous in deep learning. They are particularly useful for
images, as they reduce the number of parameters, reduce training time, and increase …

Classification of whole-brain functional connectivity MRI data with convolutional neural
networks (CNNs) has shown promise, but the complexity of these models impedes …

Abstract The Shallow and the Deep is a collection of lecture notes that offers an accessible
introduction to neural networks and machine learning in general. However, it was clear from …

The remarkable progress in computer vision over the last few years is, by and large,
attributed to deep learning, fueled by the availability of huge sets of labeled data, and paired …

The classic computational scheme of convolutional layers leverages filter banks that are
shared over all the spatial coordinates of the input, independently on external information on …

Artificial neural networks took a lot of inspiration from their biological counterparts in
becoming our best machine perceptual systems. This work summarizes some of that history …

The coarse-to-fine approach has gained significant popularity in the design of networks for
single image deblurring. Traditional methods used to employ U-shaped networks with a …