SAXELAB.ORG

The interactions of billions of neurons ultimately give rise to our thoughts and actions. Remarkably, much of our behavior is learned starting in infancy and continuing throughout our lifespan. We aim to develop a mathematical toolkit suitable for analyzing and describing learning in the brain and mind. Our current focus is on the theory of deep learning, a class of artificial neural network models that take inspiration from the brain. Alongside this theoretical work, we develop close collaborations with experimentalists to empirically test principles of learning in biological organisms... Despite the widespread practical success of deep learning methods, our theoretical understanding of the dynamics of learning in deep neural networks remains quite sparse. We attempt to bridge the gap between the theory and practice of deep learning by systematically analyzing learning dynamics for the restricted case of deep linear neural networks. Despite the linearity of their input-output map,..

Primary location: London United Kingdom