Signal processing, communications, and control have traditionally relied on classical
statistical modeling techniques. Such model-based methods utilize mathematical …

Learning to optimize (L2O) is an emerging approach that leverages machine learning to
develop optimization methods, aiming at reducing the laborious iterations of hand …

Single-pixel imaging (SPI) uses a single-pixel detector instead of a detector array with a lot
of pixels in traditional imaging techniques to realize two-dimensional or even multi …

An automatic modulation classification has a very broad application in wireless
communications. Recently, deep learning has been used to solve this problem and …

In this article, we investigate jointly sparse signal recovery and jointly sparse support
recovery in Multiple Measurement Vector (MMV) models for complex signals, which arise in …

Deep neural networks have been applied successfully to a wide variety of inverse problems
arising in computational imaging. These networks are typically trained using a forward …

Covering from photography to depth and spectral estimation, diverse computational imaging
(CI) applications benefit from the versatile modulation of coded apertures (CAs). The …

In recent years, there have been significant advances in the use of deep learning methods in
inverse problems such as denoising, compressive sensing, inpainting, and super-resolution …

Signal processing, communications, and control have traditionally relied on classical
statistical modeling techniques. Such model-based methods tend to be sensitive to …

In learning-augmented algorithms, algorithms are enhanced using information from a
machine learning algorithm. In turn, this suggests that we should tailor our machine-learning …