Speaker — Ami Wiesel (Hebrew University of Jerusalem)
Abstract — In this talk, we will discuss the use of deep learning in statistical signal processing. We will address settings in which the classical solutions are intractable and will propose modern approaches based on neural networks. We will begin with parameter estimation and focus on learning non-linear minimum variance unbiased estimators (MVUE). Next, we will switch to detection theory and focus on learning classifiers with constant false alarm rates (CFAR). In both settings, we provide deep learning methods that achieve these goals in practice, as well as theory that highlights the relations to the classical likelihood based solutions.
Learning to estimate without bias
CFARnet: deep learning for target detection with constant false alarm rate
Bio — Ami Wiesel received the B.Sc. and M.Sc. degrees in electrical engineering from Tel-Aviv University, Tel-Aviv, Israel, in 2000 and 2002, respectively, and the Ph.D. degree in electrical engineering from the Technion – Israel Institute of Technology, Haifa, Israel, in 2007. He was a postdoctoral fellow in the University of Michigan, Ann Arbor, USA, during 2007–2009. He is currently an Associate Professor in the Rachel and Selim Benin School of Computer Science and Engineering, Hebrew University of Jerusalem, Israel.