In recent years, deep learning has revolutionized the field of machine learning, for computer
vision in particular. In this approach, a deep (multilayer) artificial neural network (ANN) is …

Brains, it has recently been argued, are essentially prediction machines. They are bundles
of cells that support perception and action by constantly attempting to match incoming …

We perceive the shapes and material properties of objects quickly and reliably despite the
complexity and objective ambiguities of natural images. Typical images are highly complex …

We address the problem of visual classification with multiple features and/or multiple
instances. Motivated by the recent success of multitask joint covariate selection, we …

The auditory neural code must serve a wide range of auditory tasks that require great
sensitivity in time and frequency and be effective over the diverse array of sounds present in …

Confidence judgements, self-assessments about the quality of a subject's knowledge, are
considered a central example of metacognition. Prima facie, introspection and self-report …

Many approaches have been proposed to solve the problem of finding the optic flow field of
an image sequence. Three major classes of optic flow computation techniques can …

We present an introduction to Bayesian inference as it is used in probabilistic models of
cognitive development. Our goal is to provide an intuitive and accessible guide to the what …

Artificial intelligence (AI) is rapidly becoming one of the key technologies of this century. The
majority of results in AI thus far have been achieved using deep neural networks trained with …

A central goal in theoretical neuroscience is to predict the response properties of sensory
neurons from first principles. To this end,“efficient coding” posits that sensory neurons …