Tutorial
Geometric Deep Learning on Graphs and Manifolds
Michael Bronstein · Joan Bruna · arthur szlam · Xavier Bresson · Yann LeCun

Mon Dec 4th 02:30 -- 04:45 PM @ Hall A

In the past years, deep learning methods have achieved unprecedented performance on a broad range of problems in various fields from computer vision to speech recognition. So far research has mainly focused on developing deep learning methods for Euclidean-structured data, while many important applications have to deal with non-Euclidean structured data, such as graphs and manifolds. Such geometric data are becoming increasingly important in computer graphics and 3D vision, sensor networks, drug design, biomedicine, recommendation systems, and web applications. The adoption of deep learning in these fields has been lagging behind until recently, primarily since the non-Euclidean nature of objects dealt with makes the very definition of basic operations used in deep networks rather elusive.

The purpose of the proposed tutorial is to introduce the emerging field of geometric deep learning on graphs and manifolds, overview existing solutions and applications for this class of problems, as well as key difficulties and future research directions.

Author Information

Michael Bronstein (USI Lugano / Tel Aviv University / Intel)

Michael Bronstein is an associate professor of Informatics at USI Lugano in Switzerland, associate professor of Applied Mathematics at Tel Aviv University in Israel, and a Principal Engineer at the Intel Perceptual Computing. Michael got his Ph.D. with distinction in Computer Science from the Technion in 2007. He has held visiting appointments at Stanford, Harvard, and MIT. He is a Senior Member of the IEEE, alumnus of the Technion Excellence Program and the Academy of Achievement, ACM Distinguished Speaker, and a member of the Young Academy of Europe. His research appeared in the international media such as CNN and was recognized by numerous prestigious awards, including several best paper awards, three ERC grants (Starting Grant 2012, Proof of Concept Grant 2016, and Consolidator Grant 2016), Google Faculty Research Award (2016), Radcliffe Fellowship from the Institute for Advanced Study at Harvard University (2017), and Rudolf Diesel Industrial Fellowship from TU Munich (2017). In 2014, he was invited as a Young Scientist to the World Economic Forum, an honor bestowed on forty world's leading scientists under the age of forty. Michael is the author of over 100 papers in top scientific journals and conferences, and inventor of over 25 granted patents. He has chaired over a dozen of conferences and workshops in his field, and has served as area chair at ECCV 2016 and ICCV 2017 and as associate editor of the Computer Vision and Image Understanding journal. Besides academic work, Michael is actively involved in the industry. He has co-founded and served in leading technical and management positions at several startup companies, including Invision, an Israeli startup developing 3D sensing technology acquired by Intel in 2012.

Joan Bruna (NYU)
arthur szlam (Facebook)
Xavier Bresson (NTU)
Yann LeCun (Facebook AI Research and New York University)

Yann LeCun is Director of AI Research at Facebook, and Silver Professor of Data Science, Computer Science, Neural Science, and Electrical Engineering at New York University. He received the Electrical Engineer Diploma from ESIEE, Paris in 1983, and a PhD in Computer Science from Université Pierre et Marie Curie (Paris) in 1987. After a postdoc at the University of Toronto, he joined AT&T Bell Laboratories in Holmdel, NJ in 1988. He became head of the Image Processing Research Department at AT&T Labs-Research in 1996, and joined NYU as a professor in 2003, after a brief period as a Fellow of the NEC Research Institute in Princeton. From 2012 to 2014 he directed NYU's initiative in data science and became the founding director of the NYU Center for Data Science. He was named Director of AI Research at Facebook in late 2013 and retains a part-time position on the NYU faculty. His current interests include AI, machine learning, computer perception, mobile robotics, and computational neuroscience. He has published over 180 technical papers and book chapters on these topics as well as on neural networks, handwriting recognition, image processing and compression, and on dedicated circuits for computer perception.

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