The finding that human infants and many other animal species are sensitive to numerical
quantity has been widely interpreted as evidence for evolved, biologically determined …

A rigid-motion scattering computes adaptive invariants along translations and rotations, with
a deep convolutional network. Convolutions are calculated on the rigid-motion group, with …

An affine invariant representation is constructed with a cascade of invariants, which
preserves information for classification. A joint translation and rotation invariant …

The evolution of hearing in terrestrial animals has resulted in remarkable adaptations
enabling exquisitely sensitive sound detection by the ear and sophisticated sound analysis …

A complex-valued convolutional network (convnet) implements the repeated application of
the following composition of three operations, recursively applying the composition to an …

The human cerebral cortex is central to a wide array of cognitive functions, from vision to
language, reasoning, decision-making, and motor control. Yet, nearly a century after the …

This paper studies the generalization error of invariant classifiers. In particular, we consider
the common scenario where the classification task is invariant to certain transformations of …

Reverse-engineering the brain involves adopting and testing a hierarchy of working
hypotheses regarding the computational problems that it solves, the representations and …

One approach to computer object recognition and modeling the brain's ventral stream
involves unsupervised learning of representations that are invariant to common …

Estimating the optimal representation from sensor data has been one of the most
challenging problems in computer vision research. Given a particular task, an optimal …