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 grid-structured data, while many important applications have to deal with graph-structured data. Such geometric data are becoming increasingly important in computer graphics and 3D vision, sensor networks, drug design, biomedicine, recommendation systems, NLP and computer vision with knowledge graphs, and web applications. The purpose of this talk is to introduce convolutional neural networks architectures on graphs, as well as applications for this class of problems.
Xavier Bresson (PhD 2005, EPFL, Switzerland) is Associate Professor in Computer Science and member of the Data Science and AI Research Centre at NTU, Singapore. He is a leading researcher in the field of graph deep learning, a new framework that combines graph theory and deep learning techniques to tackle complex data domains in quantum chemistry, neuroscience, genetics, physics, computer vision and natural language processing. In 2016, he received the highly competitive Singaporean NRF Fellowship of 2.5M US$ to develop these new learning techniques. He was also awarded several research grants in the U.S. and Hong Kong. He has published more than 60 peer-reviewed papers, including NeurIPS, ICML, ICLR, JMLR. He has organized international workshops and tutorials on graph deep learning in collaboration with Facebook, NYU, and ICL such as 2018, 2019 UCLA workshops, 2017 CVPR tutorial and 2017 NeurIPS tutorial. He has been teaching undergraduate, graduate and industrial courses in data science and deep learning at EPFL (Switzerland), NTU (Singapore) and UCLA (U.S.). He is Associate editor of SIAM Journal on Imaging Sciences.