
Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing
Deep neural networks provide unprecedented performance gains in many rea...
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Semisupervised Learning in NetworkStructured Data via Total Variation Minimization
We propose and analyze a method for semisupervised learning from partia...
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Fast Deep Learning for Automatic Modulation Classification
In this work, we investigate the feasibility and effectiveness of employ...
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An Algorithm Unrolling Approach to Deep Blind Image Deblurring
Blind image deblurring remains a topic of enduring interest. Learning ba...
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Ensemble Wrapper Subsampling for Deep Modulation Classification
Subsampling of received wireless signals is important for relaxing hardw...
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Deep Unfolded Robust PCA with Application to Clutter Suppression in Ultrasound
Contrast enhanced ultrasound is a radiationfree imaging modality which ...
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Coupled Dictionary Learning for Multicontrast MRI Reconstruction
Medical imaging tasks often involve multiple contrasts, such as T1 and ...
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Tradeoffs between Convergence Speed and Reconstruction Accuracy in Inverse Problems
Solving inverse problems with iterative algorithms is popular, especiall...
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Solving Systems of Random Quadratic Equations via Truncated Amplitude Flow
This paper presents a new algorithm, termed truncated amplitude flow (TA...
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DOLPHIn  Dictionary Learning for Phase Retrieval
We propose a new algorithm to learn a dictionary for reconstructing and ...
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Sparse Nonlinear Regression: Parameter Estimation and Asymptotic Inference
We study parameter estimation and asymptotic inference for sparse nonlin...
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Performance Limits of Dictionary Learning for Sparse Coding
We consider the problem of dictionary learning under the assumption that...
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Compressive Shift Retrieval
The classical shift retrieval problem considers two signals in vector fo...
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SemiSupervised Single and MultiDomain Regression with MultiDomain Training
We address the problems of multidomain and singledomain regression bas...
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Compressed sensing for longitudinal MRI: An adaptiveweighted approach
Purpose: Repeated brain MRI scans are performed in many clinical scenari...
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Compressed Beamforming in Ultrasound Imaging
Emerging sonography techniques often require increasing the number of tr...
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The Projected GSURE for Automatic Parameter Tuning in Iterative Shrinkage Methods
Linear inverse problems are very common in signal and image processing. ...
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Hybrid AnalogDigital Beamforming for Massive MIMO Systems
In massive MIMO systems, hybrid beamforming is an essential technique fo...
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On the Sample Complexity of Multichannel Frequency Estimation via Convex Optimization
The use of multichannel data in estimating a set of frequencies is commo...
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Pilot Contamination Mitigation with Reduced RF Chains
Massive multipleinput multipleoutput (MIMO) communication is a promisi...
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AnalogtoDigital Compression: A New Paradigm for Converting Signals to Bits
Processing, storing and communicating information that originates as an ...
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On the Spectral Efficiency of Noncooperative Uplink Massive MIMO Systems
Massive multipleinput multipleoutput (MIMO) systems have been drawing ...
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Cognitive Radar Antenna Selection via Deep Learning
Direction of arrival (DoA) estimation of targets improves with the numbe...
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RAPToR: A Resampling Algorithm for PseudoPolar based Tomographic Reconstruction
We propose a stable and fast reconstruction technique for parallelbeam ...
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SubNyquist Radar: Principles and Prototypes
In the past few years, new approaches to radar signal processing have be...
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Superresolution Ultrasound Localization Microscopy through Deep Learning
Ultrasound localization microscopy has enabled superresolution vascular...
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The Global Optimization Geometry of Shallow Linear Neural Networks
We examine the squared error loss landscape of shallow linear neural net...
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Sparse Convolutional Beamforming for Ultrasound Imaging
The standard technique used by commercial medical ultrasound systems to ...
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Subspace Estimation from Incomplete Observations: A HighDimensional Analysis
We present a highdimensional analysis of three popular algorithms, name...
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Analog to Digital Cognitive Radio: Sampling, Detection and Hardware
The proliferation of wireless communications has recently created a bott...
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Sampling and Superresolution of Sparse Signals Beyond the Fourier Domain
Recovering a sparse signal from its lowpass projections in the Fourier ...
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On MIMO Channel Capacity with Output Quantization Constraints
The capacity of a MultipleInput MultipleOutput (MIMO) channel in which...
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iMAP Beamforming for High Quality High Frame Rate Imaging
We present a statistical interpretation of beamforming to overcome limit...
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Analysis of Frequency Agile Radar via Compressed Sensing
Frequency agile radar (FAR) is known to have excellent electronic counte...
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A Cognitive SubNyquist MIMO Radar Prototype
We present a prototype that demonstrates the principle of a colocated, f...
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HardwareLimited TaskBased Quantization
Quantization plays a critical role in digital signal processing systems....
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SubNyquist Radar Systems: Temporal, Spectral and Spatial Compression
Conventional radar transmits electromagnetic waves towards the targets o...
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Blind Phaseless ShortTime Fourier Transform Recovery
The problem of recovering a pair of signals from their blind phaseless s...
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Convolutional Phase Retrieval via Gradient Descent
We study the convolutional phase retrieval problem, which considers reco...
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Functional Nonlinear Sparse Models
Signal processing in inherently continuous and often nonlinear applicati...
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Deep Signal Recovery with OneBit Quantization
Machine learning, and more specifically deep learning, have shown remark...
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Dynamic Metasurface Antennas for Uplink Massive MIMO Systems
Massive multipleinput multipleoutput (MIMO) communications are the foc...
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On Multiterminal Communication over MIMO Channels with Onebit ADCs at the Receivers
The fundamental limits of communication over multipleinput multipleout...
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An Algorithm Unrolling Approach to Deep Image Deblurring
While neural networks have achieved vastly enhanced performance over tra...
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Tradeoff Between Delay and High SNR Capacity in Quantized MIMO Systems
Analogtodigital converters (ADCs) are a major contributor to the power...
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The Capacity of Memoryless Channels with Sampled Cyclostationary Gaussian Noise
Nonorthogonal communications play an important role in future communica...
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Sample Efficient Toeplitz Covariance Estimation
We study the query complexity of estimating the covariance matrix T of a...
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Coordinated Nonorthogonal Pilot Design for Massive MIMO
Pilot contamination caused by the nonorthogonality of pilots is a main l...
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Deep Learning for Interference Identification: Band, Training SNR, and Sample Selection
We study the problem of interference source identification, through the ...
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ViterbiNet: A Deep Learning Based Viterbi Algorithm for Symbol Detection
Symbol detection plays an important role in the implementation of digita...
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Yonina C. Eldar
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Professor of Electrical Engineering at Technion  Israel Institute of Technology, Adjunt Professor at Duke University