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

Compressive learning (CL) is an emerging framework that integrates signal acquisition via
compressed sensing (CS) and machine learning for inference tasks directly on a small …

Coronavirus disease 2019 (COVID-19) has rapidly become a global health concern after its
first known detection in December 2019. As a result, accurate and reliable advance warning …

As a kind of network structure increasingly studied in compressive sensing (CS), deep
unfolding networks (DUNs), which unroll the iterative reconstruction procedure as deep …

Inspired by certain optimization solvers, the deep unfolding network (DUN) usually inherits a
multi-phase structure for image compressive sensing (CS). However, in existing DUNs, the …

Financial time-series analysis and forecasting have been extensively studied over the past
decades, yet still remain as a very challenging research topic. Since the financial market is …

Compressive Learning (CL) has proven to be highly successful in executing joint signal
sampling and inference for intricate vision tasks through resource-limited Internet of Things …

In the federated learning (FL) research field, current research is confronted with several
pivotal challenges, eg, data privacy, model utility and communication efficiency …

Deep network-based image Compressive Sensing (CS) has attracted much attention in
recent years. However, there still exist the following two issues: 1) Existing methods typically …

Abstract The Machine-Learning-as-a-Service (MLaaS) framework allows one to grab low-
hanging fruit of machine learning techniques and data science, without either much …