The commercial fifth-generation (5G) wireless communications networks have already been
deployed with the aim of providing high data rates. However, the rapid growth in the number …

Deep learning bears promise for drug discovery, including advanced image analysis,
prediction of molecular structure and function, and automated generation of innovative …

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 …

In frequency division duplex mode, the downlink channel state information (CSI) should be
sent to the base station through feedback links so that the potential gains of a massive …

In this paper we propose a novel method that provides contrastive explanations justifying the
classification of an input by a black box classifier such as a deep neural network. Given an …

In order to improve CS performance of natural images, in this paper, we propose a novel
framework to design an OPtimization-INspired Explicable deep Network, dubbed OPINE …

Estimating the frequencies of elements in a data stream is a fundamental task in data
analysis and machine learning. The problem is typically addressed using streaming …

Convolutional Neural Networks (CNNs) dominate image processing but suffer from local
inductive bias, which is addressed by the transformer framework with its inherent ability to …

Compressive sensing (CS) is a mathematically elegant tool for reducing the sensor
sampling rate, potentially bringing context-awareness to a wider range of devices …

Traditional algorithms for compressive sensing recovery are computationally expensive and
are ineffective at low measurement rates. In this paper, we propose a data-driven …