T-10: Model-based deep learning in signal processing: Part 1

Over the past several years, deep learning, or more generally artificial intelligence, has spurred overwhelming research interest and attracted unprecedented attention leading to systems with far better performance than previous methods in areas such as computer vision, speech processing, and more. Standard deep learning techniques rely on vast training data to tune the weights of large general-purpose deep networks. These networks are context-agnostic, and inherit their power from the extent of the training data. On the other hand, signal processing and communication algorithms are typically derived based on adequate models of the system, data acquisition and more. When applying AI to problems in communications, array processing, and more general signal processing, it is therefore natural to seek deep learning techniques that can efficiently leverage these signal models and priors. In our labs, we have been focusing on various approaches to model-based deep networks which rely on underlying models of the system and data in order to develop deep networks tailored to the specific problem at hand. We are very interested in understanding these types of networks theoretically and also in exploring their applications to communications, radar, imaging, medical applications, microscopy and more.