Unsupervised Learning of Equivariant Space-Time Capsules
Speaker: Max Welling, University of Amsterdam
Prof. Dr. Max Welling is a research chair in Machine Learning at the University of Amsterdam and a VP Technologies at Qualcomm. He has a secondary appointment as a fellow at the Canadian Institute for Advanced Research (CIFAR). Max Welling has served as associate editor in chief of IEEE TPAMI from 2011-2015. He serves on the board of the Neurips foundation since 2015 and has been program chair and general chair of Neurips in 2013 and 2014 respectively. He was also program chair of AISTATS in 2009 and ECCV in 2016 and general chair of MIDL 2018. He is a founding board member of ELLIS. Max Welling is recipient of the ECCV Koenderink Prize in 2010. He directs the Amsterdam Machine Learning Lab (AMLAB) and co-directs the Qualcomm-UvA deep learning lab (QUVA) and the Bosch-UvA Deep Learning lab (DELTA). He is a fellow and founding board member of the European Lab for learning and Intelligent systems (ELLIS).
Equivariance is an organizing principle in deep learning that expresses how internal representation should behave under symmetry transformations. To learn equivariant neural networks, we usually must know the representation theory for the symmetry group under consideration. This raises the question, can this structure also be learned completely unsupervised. In this talk I will argue that we can use a connection between topographic representations (like the ones developed in topographic ICA) with the notion of equivariant capsules. Capsules also organize representations such that nearby filters in the topographic map are similar. This means that as we observe a stimulus over time, we expect that the activations change smoothly and slowly through this “neural space-time”. By structuring these representations as circular capsules, internal representations behave as oscillators (one oscillator per capsule), and we can predict the future by rolling forward activated oscillators. If time allows, I will try to make a connection to quantum field theory and Hinton particles inside neural networks which end up being quantum excitations of these space-time capsule oscillators.