From Quadrant (a D-Wave business), this whitepaper "Data-Efficient Machine Learning" describes a practical impediment to the application of deep neural network models when large training data sets are unavailable. Encouragingly however, it is shown that recent machine learning advances make it possible to obtain the benefits of deep neural networks by making more efficient use of training data that most practitioners do have. Quadrant leverages generative machine learning, which requires much less labeled data than common discriminative models. This is incredibly useful in countless applications, including medical imaging which is often limited to relatively small data sets (i.e. For a first case study, Siemens Healthineers partnered with Quadrant to identify surgical tools used in cataract surgery with 99.71% accuracy.

This workshop will attempt to present some of the very recent developments on non-convex analysis and optimization, as reported in diverse research fields: from machine learning and mathematical programming to statistics and theoretical computer science. We believe that this workshop can bring researchers closer, in order to facilitate a discussion regarding why tackling non-convexity is important, where it is found, why non-convex schemes work well in practice and, how we can progress further with interesting research directions and open problems.

Max Welling is a research chair in Machine Learning at the University of Amsterdam and has secondary appointments as full professor at the University of California Irvine and as a senior fellow at the Canadian Institute for Advanced Research (CIFAR). He is co-founder of "Scyfer BV" a university spin-off in deep learning. In the past he held postdoctoral positions at Caltech ('98-'00), UCL ('00-'01) and the U. Toronto ('01-'03). Max Welling has served as associate editor in chief of IEEE TPAMI from 2011-2015. He serves on the board of the NIPS foundation since 2015 and has been program chair and general chair of NIPS in 2013 and 2014, respectively.