The annual Israel Machine Vision Conference (IMVC) took place on March 6, 2018 at David InterContinental Tel Aviv.
Dr. Raja Giryes spoke at a conference on “the Relationship between the Structure in the Data and What Deep Learning Can Learn”.
The past six years have seen a dramatic increase in the performance of recognition systems due to the introduction of deep architectures for feature learning and classification. However, the mathematical reasons for this success remain elusive. In this talk, we will briefly survey some existing theory of deep learning. In particular, we will focus on data structure based theory and discuss two recent developments. The first studies the generalization error of deep neural network. The second focuses on solving minimization problems with neural networks.
Raja Giryes is a senior lecturer (assistant professor) in the School of Electrical Engineering at Tel Aviv University. He received his B.Sc, M.Sc., and PhD degrees from the Computer Science Department at the Technion, and was a postdoc at the lab of Prof. G. Sapiro at Duke University. His research interests include signal and image processing and machine learning, and in particular, deep learning and sparse representations.
Raja received numerous grants and awards for his research including the ERC-StG grant and the Azrieli Fellowship. He organized workshops and tutorials on deep learning in leading conference such as ICML, ICCV, CVPR, CDC and ACCV.
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