Professor of Computer Science, Technion - Israel Institute of
Technology
Montreal Chair in Sciences, Henry and Marilyn Taub Faculty of
Computer Science, Technion
Founder of the Technion Geometric Image Processing Lab, Technion
Deep learning is a disruptive line of research that changes the way computational problems are being addressed
and solved. Many parameters are optimized for, and tuned to train a given computational
architecture to classify, segment, identify, and reconstruct objects. This methodology works great as long
as there is some assumption about the size of the data, or its spatial or temporal shift invariance property
that allow convolutional neural networks to operate on the given data. The question we will address is
what can be done for geometric structures for which there is no linear shift invariance mechanism to rely
on. We will relate to matching geometric structures, measuring geodesic distances, classifying objects, and
ways to import axiomatic constructions into the learning arena, that give birth to novel semi-supervised
learning procedures. Finally, I will comment on a new and interesting line of research we have started to
explore dealing with computational pathology.