Geometric Deep Learning - Michael M. Bronstein

Video of the talk by Prof. Dr. Michael M. Bronstein at our fourth Deep Learning in Action series on 1. August 2016.

Abstract: "In recent years, more and more data science applications have to deal with a somewhat unusual kind of data residing on non-Euclidean geometric domains such as manifolds or graphs. For instance, in social networks, the characteristics of users can be modeled as signals on the vertices of the social graph. In genetics, gene expression data are modeled as signals defined on the regulatory network. In computer graphics and vision, 3D objects are modeled as Riemannian manifolds (surfaces) endowed with properties such as color texture. Furthermore, modeling high-dimensional data with graphs is an increasingly popular trend in general data science, where graphs are used to describe the low-dimensional intrinsic structure of the data.

The complexity of geometric data and the availability of very large datasets (in the case of social networks, of the billion-scale) make it tempting and very desirable to resort to machine learning techniques. Yet, currently available learning methods have been developed primarily for Euclidean data and are not suitable for the non-Euclidean setting. In this talk, I will overview the emerging field of deep learning on geometric data and show examples of applications from network analysis and computer vision and graphics."

Bio: Michael Bronstein is a professor in the Faculty of Informatics at the University of Lugano (USI), Switzerland and a Research Scientist at the Perceptual Computing group, Intel, Israel. He held visiting appointments at Politecnico di Milano (2008), Stanford university (2009), and University of Verona (2010, 2014). Michael got his B.Sc. in Electrical Engineering (2002) and Ph.D. in Computer Science (2007), both from the Technion, Israel. His main research interests are theoretical and computational methods in spectral and metric geometry and their application to problems in computer vision, pattern recognition, shape analysis, computer graphics, image processing, and machine learning. His research appeared in international media and was recognized by numerous awards. In 2012, Michael received the highly-competitive European Research Council (ERC) starting grant. In 2014, he was invited as a Young Scientist to the World Economic Forum New Champions meeting in China, an honor bestowed on forty world's leading scientists under the age of 40. Besides academic work, Michael is actively involved in the industry. He was the co-founder of the Silicon Valley start-up company Novafora, where he served as Vice President of technology (masked), responsible for the development of algorithms for large-scale video analysis. He was one of the principal inventors and technologists at Invision, an Israeli startup developing 3D sensing technology acquired by Intel in 2012 and released under the RealSense brand.

[saisagar]

Audio is not clear. May we get cleaner video?