The desire to reduce the dependence on curated, labeled datasets and to leverage the vast
quantities of unlabeled data has triggered renewed interest in unsupervised (or self …

Progress in deep learning has spawned great successes in many engineering applications.
As a prime example, convolutional neural networks, a type of feedforward neural networks …

We introduce a deep recurrent neural network architecture that approximates visual cortical
circuits. We show that this architecture, which we refer to as the gamma-net, learns to solve …

Forming perceptual groups and individuating objects in visual scenes is an essential step
towards visual intelligence. This ability is thought to arise in the brain from computations …

The retinal image is insufficient for determining what is “out there,” because many different
real-world geometries could produce any given retinal image. Thus, the visual system must …

For a long time, economists have assumed that we were cold, self-centred, rational decision
makers–so-called Homo economicus; the last few decades have shattered this view. The …

Aiming at the convergence between Earth observation (EO) Big Data and Artificial General
Intelligence (AGI), this two-part paper identifies an innovative, but realistic EO optical …

Deep neural networks (DNNs) have revolutionized computer science and are now widely
used for neuroscientific research. A hot debate has ensued about the usefulness of DNNs as …

Visual attention helps achieve robust perception under noise, corruption, and distribution
shifts in human vision, which are areas where modern neural networks still fall short. We …

Imagine trying to track one particular fruitfly in a swarm of hundreds. Higher biological visual
systems have evolved to track moving objects by relying on both their appearance and their …