The recent amazing success of deep learning has been mainly in discriminative learning, that is, classification and regression. An important factor for this success has been, besides Moore's law, the availability of large labeled datasets. However, it is not clear whether in the future the amount of available labels grows as fast as the amount of unlabeled data, providing one argument to be interested in unsupervised and semi-supervised learning.
Besides this there are a number of other reasons why unsupervised learning is still important, such as the fact that data in the life sciences often has many more features than instances (p>>n), the fact that probabilities over feature space are useful for planning and control problems and the fact that complex simulator models are the norm in the sciences. In this talk I will discuss deep generative models that can be jointly trained with discriminative models and that facilitate semi-supervised learning. I will discuss recent progress in learning and Bayesian inference in these "variational auto-encoders". I will then extend the deep generative models to the class of simulators for which no tractable likelihood exists and discuss new Bayesian inference procedures to fit these models to data.
Biography: Max Welling is a Professor of Computer Science at the University of Amsterdam and the University of California Irvine. In the past he held postdoctoral positions at Caltech ('98-'00), UCL ('00-'01) and the U. Toronto ('01-'03). He received his PhD in '98 under supervision of Nobel laureate Prof. G. 't Hooft.
Max Welling served as associate editor in chief of IEEE TPAMI from 2011-2015. He serves on the editorial boards of JMLR and JML and was an associate editor for Neurocomputing, JCGS and TPAMI. In 2009 he was conference chair for AISTATS, in 2013 he was be program chair for NIPS, in 2014 he was the general chair for NIPS and in 2016 he will be a program chair at ECCV. He received multiple grants from NSF, NIH, ONR, NWO, Facebook, Yahoo and Google, among which an NSF career grant in 2005. He is recipient of the ECCV Koenderink Prize in 2010 and the best paper award at ICML 2012.
Welling is currently the director of the master program in artificial intelligence at the UvA and he is in the scientific board of the newly opened Data Science Research Center in Amsterdam. He is also an associate fellow of the Neural Computation and Adaptive Perception Program at the Canadian Institute for Advanced Research. Welling’s research focuses on large-scale statistical learning. He has made contributions in Bayesian learning, approximate inference in graphical models and visual object recognition. He has over 150 academic publications.