Since their inception in the 1930–1960s, the research disciplines of computational imaging
and machine learning have followed parallel tracks and, during the last two decades …

Machine learning (ML) has been widely applied to the upper layers of wireless
communication systems for various purposes, such as deployment of cognitive radio and …

Recent work in machine learning shows that deep neural networks can be used to solve a
wide variety of inverse problems arising in computational imaging. We explore the central …

Compressive sensing (CS) is an effective technique for reconstructing image from a small
amount of sampled data. It has been widely applied in medical imaging, remote sensing …

DL has shown great potential to revolutionize communication systems. This article provides
an overview of the recent advancements in DL-based physical layer communications. DL …

We introduce a model-based image reconstruction framework with a convolution neural
network (CNN)-based regularization prior. The proposed formulation provides a systematic …

With the aim of developing a fast yet accurate algorithm for compressive sensing (CS)
reconstruction of natural images, we combine in this paper the merits of two existing …

End-to-end learning of communications systems is a fascinating novel concept that has so
far only been validated by simulations for block-based transmissions. It allows learning of …

In this paper, we consider multiple-input-multiple-output detection using deep neural
networks. We introduce two different deep architectures: a standard fully connected multi …

We propose and study a method called FLOT that estimates scene flow on point clouds. We
start the design of FLOT by noticing that scene flow estimation on point clouds reduces to …