Search Results for author: Yonina C. Eldar

Found 90 papers, 14 papers with code

Residual Recovery Algorithm For Modulo Sampling

no code implementations7 Oct 2021 Eyar Azar, Satish Mulleti, Yonina C. Eldar

Existing recovery algorithms to recover the signal from its modulo samples operate at a high sampling rate and are not robust in the presence of noise.

Deep Unrolled Recovery in Sparse Biological Imaging

no code implementations28 Sep 2021 Yair Ben Sahel, John P. Bryan, Brian Cleary, Samouil L. Farhi, Yonina C. Eldar

Deep algorithm unrolling has emerged as a powerful model-based approach to develop deep architectures that combine the interpretability of iterative algorithms with the performance gains of supervised deep learning, especially in cases of sparse optimization.

Successive Convex Approximation for Phase Retrieval with Dictionary Learning

no code implementations13 Sep 2021 Tianyi Liu, Andreas M. Tillmann, Yang Yang, Yonina C. Eldar, Marius Pesavento

The second algorithm, referred to as SCAphase, uses auxiliary variables and is favorable in the case of highly diverse mixture models.

Dictionary Learning

Near-field Wireless Power Transfer for 6G Internet-of-Everything Mobile Networks: Opportunities and Challenges

no code implementations17 Aug 2021 Haiyang Zhang, Nir Shlezinger, Francesco Guidi, Davide Dardari, Mohammadreza F. Imani, Yonina C. Eldar

Radiating wireless power transfer (WPT) brings forth the possibility to cost-efficiently charge wireless devices without requiring a wiring infrastructure.

Deep Algorithm Unrolling for Biomedical Imaging

no code implementations15 Aug 2021 Yuelong Li, Or Bar-Shira, Vishal Monga, Yonina C. Eldar

In this chapter, we review biomedical applications and breakthroughs via leveraging algorithm unrolling, an important technique that bridges between traditional iterative algorithms and modern deep learning techniques.

Image Generation

Multimodal Unrolled Robust PCA for Background Foreground Separation

no code implementations13 Aug 2021 Spencer Markowitz, Corey Snyder, Yonina C. Eldar, Minh N. Do

Background foreground separation (BFS) is a popular computer vision problem where dynamic foreground objects are separated from the static background of a scene.

Compressed Ultrasound Imaging:from Sub-Nyquist Rates to Super-Resolution

no code implementations23 Jul 2021 Oded Drori, Alon Mamistvalov, Oren Solomon, Yonina C. Eldar

This huge and promising market is constantly driven by new imaging and processing techniques.


Learned super resolution ultrasound for improved breast lesion characterization

no code implementations12 Jul 2021 Or Bar-Shira, Ahuva Grubstein, Yael Rapson, Dror Suhami, Eli Atar, Keren Peri-Hanania, Ronnie Rosen, Yonina C. Eldar

This study demonstrates the feasibility of in vivo human super resolution, based on a clinical scanner, to increase US specificity for different breast lesions and promotes the use of US in the diagnosis of breast pathologies.


Graph Signal Restoration Using Nested Deep Algorithm Unrolling

no code implementations30 Jun 2021 Masatoshi Nagahama, Koki Yamada, Yuichi Tanaka, Stanley H. Chan, Yonina C. Eldar

Since the proposed restoration methods are based on iterations of a (convex) optimization algorithm, the method is interpretable and keeps the number of parameters small because we only need to tune graph-independent regularization parameters.


FRaC: FMCW-Based Joint Radar-Communications System via Index Modulation

no code implementations28 Jun 2021 Dingyou Ma, Nir Shlezinger, Tianyao Huang, Yimin Liu, Yonina C. Eldar

The proposed FMCW-based radar-communications system (FRaC) operates at reduced cost and complexity by transmitting with a reduced number of radio frequency modules, combined with narrowband FMCW signalling.

Autonomous Vehicles

A Wasserstein Minimax Framework for Mixed Linear Regression

1 code implementation14 Jun 2021 Theo Diamandis, Yonina C. Eldar, Alireza Fallah, Farzan Farnia, Asuman Ozdaglar

We propose an optimal transport-based framework for MLR problems, Wasserstein Mixed Linear Regression (WMLR), which minimizes the Wasserstein distance between the learned and target mixture regression models.

Federated Learning

Beam Focusing for Near-Field Multi-User MIMO Communications

no code implementations27 May 2021 Haiyang Zhang, Nir Shlezinger, Francesco Guidi, Davide Dardari, Mohammadreza F. Imani, Yonina C. Eldar

As the ability to achieve beam focusing is dictated by the transmit antenna, we study near-field signaling considering different antenna structures, including fully-digital architectures, hybrid phase shifter-based precoders, and the emerging dynamic metasurface antenna (DMA) architecture for massive MIMO arrays.

Phase-Space Function Recovery for Moving Target Imaging in SAR by Convex Optimization

no code implementations5 May 2021 Sean Thammakhoune, Bariscan Yonel, Eric Mason, Birsen Yazıcı, Yonina C. Eldar

In this paper, we present an approach for ground moving target imaging (GMTI) and velocity recovery using synthetic aperture radar.

Federated Learning: A Signal Processing Perspective

no code implementations31 Mar 2021 Tomer Gafni, Nir Shlezinger, Kobi Cohen, Yonina C. Eldar, H. Vincent Poor

Learning in a federated manner differs from conventional centralized machine learning, and poses several core unique challenges and requirements, which are closely related to classical problems studied in the areas of signal processing and communications.

Federated Learning

Bayesian Estimation of Graph Signals

no code implementations29 Mar 2021 Ariel Kroizer, Tirza Routtenberg, Yonina C. Eldar

We show that the proposed sample-GSP estimators outperform the sample-LMMSE estimator for a limited training dataset and that the parametric GSP-LMMSE estimators are more robust to topology changes in the form of adding/removing vertices/edges.

Deep Unfolded Recovery of Sub-Nyquist Sampled Ultrasound Image

no code implementations1 Mar 2021 Alon Mamistvalov, Yonina C. Eldar

This necessitates sampling rates exceeding the Nyquist rate and the use of a large number of antenna elements to ensure sufficient image quality.

Structured LISTA for Multidimensional Harmonic Retrieval

no code implementations23 Feb 2021 Rong Fu, Yimin Liu, Tianyao Huang, Yonina C. Eldar

In this paper, we show that the mutual inhibition matrix of a MHR problem naturally has a Toeplitz structure, which means that the degrees of freedom (DoF) of the matrix can be reduced from a quadratic order to a linear order.

Adaptive Quantization of Model Updates for Communication-Efficient Federated Learning

no code implementations8 Feb 2021 Divyansh Jhunjhunwala, Advait Gadhikar, Gauri Joshi, Yonina C. Eldar

Communication of model updates between client nodes and the central aggregating server is a major bottleneck in federated learning, especially in bandwidth-limited settings and high-dimensional models.

Federated Learning Quantization

LoRD-Net: Unfolded Deep Detection Network with Low-Resolution Receivers

1 code implementation5 Feb 2021 Shahin Khobahi, Nir Shlezinger, Mojtaba Soltanalian, Yonina C. Eldar

The need to recover high-dimensional signals from their noisy low-resolution quantized measurements is widely encountered in communications and sensing.

Image Restoration by Deep Projected GSURE

no code implementations4 Feb 2021 Shady Abu-Hussein, Tom Tirer, Se Young Chun, Yonina C. Eldar, Raja Giryes

In the first one, where no explicit prior is used, we show that the proposed approach outperforms other internal learning methods, such as DIP.

Deblurring Image Restoration +1

Cramér-Rao Bound Optimization for Joint Radar-Communication Design

no code implementations29 Jan 2021 Fan Liu, Ya-Feng Liu, Ang Li, Christos Masouros, Yonina C. Eldar

We employ the Cram\'er-Rao bound (CRB) as a performance metric of target estimation, under both point and extended target scenarios.

Unitary Approximate Message Passing for Sparse Bayesian Learning

no code implementations25 Jan 2021 Man Luo, Qinghua Guo, Ming Jin, Yonina C. Eldar, Defeng, Huang, Xiangming Meng

Sparse Bayesian learning (SBL) can be implemented with low complexity based on the approximate message passing (AMP) algorithm.

Variational Inference

Model-Based Machine Learning for Communications

no code implementations12 Jan 2021 Nir Shlezinger, Nariman Farsad, Yonina C. Eldar, Andrea J. Goldsmith

We present an introduction to model-based machine learning for communication systems.

Bayesian Federated Learning over Wireless Networks

no code implementations31 Dec 2020 Seunghoon Lee, Chanho Park, Song-Nam Hong, Yonina C. Eldar, Namyoon Lee

This paper proposes a Bayesian federated learning (BFL) algorithm to aggregate the heterogeneous quantized gradient information optimally in the sense of minimizing the mean-squared error (MSE).

Federated Learning

Phase Transitions in Frequency Agile Radar Using Compressed Sensing

no code implementations30 Dec 2020 Yuhan Li, Tianyao Huang, Xingyu Xu, Yimin Liu, Yonina C. Eldar

FAR has improved anti-jamming performance over traditional pulse-Doppler radars under complex electromagnetic circumstances.

Unambiguous Delay-Doppler Recovery from Random Phase Coded Pulses

no code implementations22 Dec 2020 Xiang Liu, Deborah Cohen, Tianyao Huang, Yimin Liu, Yonina C. Eldar

Our method encodes each pulse with a random phase, varying from pulse to pulse, and then processes the received samples jointly to resolve the range ambiguity.

Model-Based Deep Learning

no code implementations15 Dec 2020 Nir Shlezinger, Jay Whang, Yonina C. Eldar, Alexandros G. Dimakis

We are interested in hybrid techniques that combine principled mathematical models with data-driven systems to benefit from the advantages of both approaches.

FlowStep3D: Model Unrolling for Self-Supervised Scene Flow Estimation

1 code implementation CVPR 2021 Yair Kittenplon, Yonina C. Eldar, Dan Raviv

Estimating the 3D motion of points in a scene, known as scene flow, is a core problem in computer vision.

Scene Flow Estimation

Statistical model-based evaluation of neural networks

1 code implementation18 Nov 2020 Sandipan Das, Prakash B. Gohain, Alireza M. Javid, Yonina C. Eldar, Saikat Chatterjee

Using a statistical model-based data generation, we develop an experimental setup for the evaluation of neural networks (NNs).

Deep Networks for Direction-of-Arrival Estimation in Low SNR

no code implementations17 Nov 2020 Georgios K. Papageorgiou, Mathini Sellathurai, Yonina C. Eldar

The proposed architecture demonstrates enhanced robustness in the presence of noise, and resilience to a small number of snapshots.

Direction of Arrival Estimation Multi-Label Classification

Compressed Fourier-Domain Convolutional Beamforming for Wireless Ultrasound imaging

no code implementations25 Oct 2020 Alon Mamistvalov, Yonina C. Eldar

Our results pave the way towards wireless US and demonstrate that high resolution US images can be produced using sub-Nyquist sampling and a small number of receiving channels.

Signal Processing Image and Video Processing

Unfolding Neural Networks for Compressive Multichannel Blind Deconvolution

no code implementations22 Oct 2020 Bahareh Tolooshams, Satish Mulleti, Demba Ba, Yonina C. Eldar

We propose a learned-structured unfolding neural network for the problem of compressive sparse multichannel blind-deconvolution.

Unrolling of Deep Graph Total Variation for Image Denoising

1 code implementation21 Oct 2020 Huy Vu, Gene Cheung, Yonina C. Eldar

While deep learning (DL) architectures like convolutional neural networks (CNNs) have enabled effective solutions in image denoising, in general their implementations overly rely on training data, lack interpretability, and require tuning of a large parameter set.

Image Denoising

A Deep-Unfolded Reference-Based RPCA Network For Video Foreground-Background Separation

no code implementations2 Oct 2020 Huynh Van Luong, Boris Joukovsky, Yonina C. Eldar, Nikos Deligiannis

This paper proposes a new deep-unfolding-based network design for the problem of Robust Principal Component Analysis (RPCA) with application to video foreground-background separation.

BiLiMO: Bit-Limited MIMO Radar via Task-Based Quantization

no code implementations1 Oct 2020 Feng Xi, Nir Shlezinger, Yonina C. Eldar

One of the reasons for this difficulty stems from the increased cost and power consumption required by analog-to-digital convertors (ADCs) in acquiring the multiple waveforms at the radar receiver.


Over-the-Air Federated Learning from Heterogeneous Data

1 code implementation27 Sep 2020 Tomer Sery, Nir Shlezinger, Kobi Cohen, Yonina C. Eldar

Our analysis reveals the ability of COTAF to achieve a convergence rate similar to that achievable over error-free channels.

Federated Learning

eSampling: Energy Harvesting ADCs

no code implementations16 Jul 2020 Neha Jain, Nir Shlezinger, Bhawna Tiwari, Yonina C. Eldar, Anubha Gupta, Vivek Ashok Bohara, Pydi Ganga Bahubalindruni

We analyze the tradeoff between the ability to recover the sampled signal and the energy harvested, and provide guidelines for setting the sampling rate in the light of accuracy and energy constraints.


Massive MIMO As an Extreme Learning Machine

no code implementations1 Jul 2020 Dawei Gao, Qinghua Guo, Yonina C. Eldar

This work shows that a massive multiple-input multiple-output (MIMO) system with low-resolution analog-to-digital converters (ADCs) forms a natural extreme learning machine (ELM).

Dynamic Metasurface Antennas for 6G Extreme Massive MIMO Communications

no code implementations14 Jun 2020 Nir Shlezinger, George C. Alexandropoulos, Mohammadreza F. Imani, Yonina C. Eldar, David R. Smith

Next generation wireless base stations and access points will transmit and receive using extremely massive numbers of antennas.

UVeQFed: Universal Vector Quantization for Federated Learning

1 code implementation5 Jun 2020 Nir Shlezinger, Mingzhe Chen, Yonina C. Eldar, H. Vincent Poor, Shuguang Cui

We show that combining universal vector quantization methods with FL yields a decentralized training system in which the compression of the trained models induces only a minimum distortion.

Federated Learning Quantization

Learned Factor Graphs for Inference from Stationary Time Sequences

no code implementations5 Jun 2020 Nir Shlezinger, Nariman Farsad, Yonina C. Eldar, Andrea J. Goldsmith

Learned factor graph can be realized using compact neural networks that are trainable using small training sets, or alternatively, be used to improve upon existing deep inference systems.

Sleep Stage Detection

Graph Unrolling Networks: Interpretable Neural Networks for Graph Signal Denoising

no code implementations1 Jun 2020 Siheng Chen, Yonina C. Eldar, Lingxiao Zhao

We unroll an iterative denoising algorithm by mapping each iteration into a single network layer where the feed-forward process is equivalent to iteratively denoising graph signals.


Ensemble Wrapper Subsampling for Deep Modulation Classification

1 code implementation10 May 2020 Sharan Ramjee, Shengtai Ju, Diyu Yang, Xiaoyu Liu, Aly El Gamal, Yonina C. Eldar

Subsampling of received wireless signals is important for relaxing hardware requirements as well as the computational cost of signal processing algorithms that rely on the output samples.

Classification Feature Selection +1

Learned SPARCOM: Unfolded Deep Super-Resolution Microscopy

1 code implementation20 Apr 2020 Gili Dardikman-Yoffe, Yonina C. Eldar

The use of photo-activated fluorescent molecules to create long sequences of low emitter-density diffraction-limited images enables high-precision emitter localization, but at the cost of low temporal resolution.

Image and Video Processing

Spatial Modulation for Joint Radar-Communications Systems: Design, Analysis, and Hardware Prototype

no code implementations23 Mar 2020 Dingyou Ma, Nir Shlezinger, Tianyao Huang, Yariv Shavit, Moshe Namer, Yimin Liu, Yonina C. Eldar

For the radar subsystem, our experiments show that the spatial agility induced by the GSM transmission improves the angular resolution and reduces the sidelobe level in the transmit beam pattern compared to using fixed antenna allocations.

Sampling Signals on Graphs: From Theory to Applications

no code implementations9 Mar 2020 Yuichi Tanaka, Yonina C. Eldar, Antonio Ortega, Gene Cheung

In this article, we review current progress on sampling over graphs focusing on theory and potential applications.

Data-Driven Symbol Detection via Model-Based Machine Learning

no code implementations14 Feb 2020 Nariman Farsad, Nir Shlezinger, Andrea J. Goldsmith, Yonina C. Eldar

The design of symbol detectors in digital communication systems has traditionally relied on statistical channel models that describe the relation between the transmitted symbols and the observed signal at the receiver.

DeepSIC: Deep Soft Interference Cancellation for Multiuser MIMO Detection

1 code implementation8 Feb 2020 Nir Shlezinger, Rong Fu, Yonina C. Eldar

In multiuser multiple-input multiple-output (MIMO) setups, where multiple symbols are simultaneously transmitted, accurate symbol detection is challenging.

Data-Driven Factor Graphs for Deep Symbol Detection

no code implementations31 Jan 2020 Nir Shlezinger, Nariman Farsad, Yonina C. Eldar, Andrea J. Goldsmith

In particular, we propose to use machine learning (ML) tools to learn the factor graph, instead of the overall system task, which in turn is used for inference by message passing over the learned graph.

Sparse Array Design via Fractal Geometries

no code implementations5 Jan 2020 Regev Cohen, Yonina C. Eldar

To that end, we introduce a fractal array design in which a generator array is recursively expanded according to its difference coarray.

Deep Task-Based Quantization

no code implementations1 Aug 2019 Nir Shlezinger, Yonina C. Eldar

In this work we design data-driven task-oriented quantization systems with scalar ADCs, which determine how to map an analog signal into its digital representation using deep learning tools.


Deep Neural Network Symbol Detection for Millimeter Wave Communications

no code implementations25 Jul 2019 Yun Liao, Nariman Farsad, Nir Shlezinger, Yonina C. Eldar, Andrea J. Goldsmith

This paper proposes to use a deep neural network (DNN)-based symbol detector for mmWave systems such that CSI acquisition can be bypassed.

Deep learning in ultrasound imaging

no code implementations5 Jul 2019 Ruud JG van Sloun, Regev Cohen, Yonina C. Eldar

We consider deep learning strategies in ultrasound systems, from the front-end to advanced applications.


ViterbiNet: A Deep Learning Based Viterbi Algorithm for Symbol Detection

no code implementations26 May 2019 Nir Shlezinger, Nariman Farsad, Yonina C. Eldar, Andrea J. Goldsmith

Our numerical evaluations demonstrate that the performance of ViterbiNet, which is ignorant of the CSI, approaches that of the CSI-based Viterbi algorithm, and is capable of tracking time-varying channels without needing instantaneous CSI or additional training data.


Deep Learning for Interference Identification: Band, Training SNR, and Sample Selection

1 code implementation16 May 2019 Xiwen Zhang, Tolunay Seyfi, Shengtai Ju, Sharan Ramjee, Aly El Gamal, Yonina C. Eldar

We study the problem of interference source identification, through the lens of recognizing one of 15 different channels that belong to 3 different wireless technologies: Bluetooth, Zigbee, and WiFi.

Classification General Classification

Sample Efficient Toeplitz Covariance Estimation

no code implementations14 May 2019 Yonina C. Eldar, Jerry Li, Cameron Musco, Christopher Musco

In addition to results that hold for any Toeplitz $T$, we further study the important setting when $T$ is close to low-rank, which is often the case in practice.

Complex Trainable ISTA for Linear and Nonlinear Inverse Problems

no code implementations16 Apr 2019 Satoshi Takabe, Tadashi Wadayama, Yonina C. Eldar

Complex-field signal recovery problems from noisy linear/nonlinear measurements appear in many areas of signal processing and wireless communications.

Deep Algorithm Unrolling for Blind Image Deblurring

no code implementations9 Feb 2019 Yuelong Li, Mohammad Tofighi, Junyi Geng, Vishal Monga, Yonina C. Eldar

We then unroll the algorithm to construct a neural network for image deblurring which we refer to as Deep Unrolling for Blind Deblurring (DUBLID).

Blind Image Deblurring

An Algorithm Unrolling Approach to Deep Image Deblurring

no code implementations9 Feb 2019 Yuelong Li, Mohammad Tofighi, Vishal Monga, Yonina C. Eldar

We first present an iterative algorithm that may be considered a generalization of the traditional total-variation regularization method on the gradient domain, and subsequently unroll the half-quadratic splitting algorithm to construct a neural network.

Blind Image Deblurring

Semi-supervised Learning in Network-Structured Data via Total Variation Minimization

no code implementations28 Jan 2019 Alexander Jung, Alfred O. Hero III, Alexandru Mara, Saeed Jahromi, Ayelet Heimowitz, Yonina C. Eldar

This lends naturally to learning the labels by total variation (TV) minimization, which we solve by applying a recently proposed primal-dual method for non-smooth convex optimization.

Fast Deep Learning for Automatic Modulation Classification

1 code implementation16 Jan 2019 Sharan Ramjee, Shengtai Ju, Diyu Yang, Xiaoyu Liu, Aly El Gamal, Yonina C. Eldar

We then study algorithms to reduce the training time by minimizing the size of the training data set, while incurring a minimal loss in classification accuracy.

Classification General Classification

Deep Signal Recovery with One-Bit Quantization

no code implementations30 Nov 2018 Shahin Khobahi, Naveed Naimipour, Mojtaba Soltanalian, Yonina C. Eldar

Machine learning, and more specifically deep learning, have shown remarkable performance in sensing, communications, and inference.


Deep Unfolded Robust PCA with Application to Clutter Suppression in Ultrasound

no code implementations20 Nov 2018 Oren Solomon, Regev Cohen, Yi Zhang, Yi Yang, He Qiong, Jianwen Luo, Ruud J. G. van Sloun, Yonina C. Eldar

We compare the performance of the suggested deep network on both simulations and in-vivo rat brain scans, with a commonly practiced deep-network architecture and the fast iterative shrinkage algorithm, and show that our architecture exhibits better image quality and contrast.


Functional Nonlinear Sparse Models

no code implementations1 Nov 2018 Luiz. F. O. Chamon, Yonina C. Eldar, Alejandro Ribeiro

Even if they are, recovering sparse solutions using convex relaxations requires assumptions that may be hard to meet in practice.

Robust classification Spectrum Cartography +1

Coupled Dictionary Learning for Multi-contrast MRI Reconstruction

1 code implementation26 Jun 2018 Pingfan Song, Lior Weizman, Joao F. C. Mota, Yonina C. Eldar, Miguel R. D. Rodrigues

In this paper, we propose a Coupled Dictionary Learning based multi-contrast MRI reconstruction (CDLMRI) approach to leverage an available guidance contrast to restore the target contrast.

Denoising Dictionary Learning +1

Dictionary Learning for Adaptive GPR Landmine Classification

no code implementations24 May 2018 Fabio Giovanneschi, Kumar Vijay Mishra, Maria Antonia Gonzalez-Huici, Yonina C. Eldar, Joachim H. G. Ender

For the case of abandoned anti-personnel landmines classification, we compare the performance of K-SVD with three online algorithms: classical Online Dictionary Learning, its correlation-based variant, and DOMINODL.

Classification Dictionary Learning +3

Subspace Estimation from Incomplete Observations: A High-Dimensional Analysis

no code implementations17 May 2018 Chuang Wang, Yonina C. Eldar, Yue M. Lu

In addition to providing asymptotically exact predictions of the dynamic performance of the algorithms, our high-dimensional analysis yields several insights, including an asymptotic equivalence between Oja's method and GROUSE, and a precise scaling relationship linking the amount of missing data to the signal-to-noise ratio.

Super-resolution Ultrasound Localization Microscopy through Deep Learning

no code implementations20 Apr 2018 Ruud J. G. van Sloun, Oren Solomon, Matthew Bruce, Zin Z. Khaing, Hessel Wijkstra, Yonina C. Eldar, Massimo Mischi

This method, which we term Deep Ultrasound Localization Microscopy (Deep-ULM), exploits modern deep learning strategies and employs a convolutional neural network to perform localization microscopy in dense scenarios.


Cognitive Radar Antenna Selection via Deep Learning

no code implementations27 Feb 2018 Ahmet M. Elbir, Kumar Vijay Mishra, Yonina C. Eldar

Direction of arrival (DoA) estimation of targets improves with the number of elements employed by a phased array radar antenna.

General Classification Multi-class Classification

Convolutional Phase Retrieval via Gradient Descent

no code implementations3 Dec 2017 Qing Qu, Yuqian Zhang, Yonina C. Eldar, John Wright

We study the convolutional phase retrieval problem, of recovering an unknown signal $\mathbf x \in \mathbb C^n $ from $m$ measurements consisting of the magnitude of its cyclic convolution with a given kernel $\mathbf a \in \mathbb C^m $.

Sparsity-Based Super Resolution for SEM Images

no code implementations29 Aug 2017 Shahar Tsiper, Or Dicker, Idan Kaizerman, Zeev Zohar, Mordechai Segev, Yonina C. Eldar

The scanning electron microscope (SEM) produces an image of a sample by scanning it with a focused beam of electrons.

Dictionary Learning Super-Resolution

Solving Systems of Random Quadratic Equations via Truncated Amplitude Flow

no code implementations26 May 2016 Gang Wang, Georgios B. Giannakis, Yonina C. Eldar

This paper presents a new algorithm, termed \emph{truncated amplitude flow} (TAF), to recover an unknown vector $\bm{x}$ from a system of quadratic equations of the form $y_i=|\langle\bm{a}_i,\bm{x}\rangle|^2$, where $\bm{a}_i$'s are given random measurement vectors.

DOLPHIn - Dictionary Learning for Phase Retrieval

no code implementations6 Feb 2016 Andreas M. Tillmann, Yonina C. Eldar, Julien Mairal

We propose a new algorithm to learn a dictionary for reconstructing and sparsely encoding signals from measurements without phase.

Dictionary Learning

Sparse Nonlinear Regression: Parameter Estimation and Asymptotic Inference

no code implementations14 Nov 2015 Zhuoran Yang, Zhaoran Wang, Han Liu, Yonina C. Eldar, Tong Zhang

To recover $\beta^*$, we propose an $\ell_1$-regularized least-squares estimator.

On the Minimax Risk of Dictionary Learning

no code implementations20 Jul 2015 Alexander Jung, Yonina C. Eldar, Norbert Görtz

The main conceptual contribution of this paper is the adaption of the information-theoretic approach to minimax estimation for the DL problem in order to derive lower bounds on the worst case MSE of any DL scheme.

Dictionary Learning

Compressed sensing for longitudinal MRI: An adaptive-weighted approach

no code implementations10 Jul 2014 Lior Weizman, Yonina C. Eldar, Dafna Ben Bashat

Methods: The proposed approach utilizes the possible similarity of the repeated scans in longitudinal MRI studies.

Image Reconstruction

Performance Limits of Dictionary Learning for Sparse Coding

no code implementations17 Feb 2014 Alexander Jung, Yonina C. Eldar, Norbert Görtz

We consider the problem of dictionary learning under the assumption that the observed signals can be represented as sparse linear combinations of the columns of a single large dictionary matrix.

Dictionary Learning

Compressive Shift Retrieval

no code implementations20 Mar 2013 Henrik Ohlsson, Yonina C. Eldar, Allen Y. Yang, S. Shankar Sastry

The problem is of great importance in many applications and is typically solved by maximizing the cross-correlation between the two signals.

Compressive Sensing

Acceleration of Randomized Kaczmarz Method via the Johnson-Lindenstrauss Lemma

no code implementations25 Aug 2010 Yonina C. Eldar, Deanna Needell

The Kaczmarz method is an algorithm for finding the solution to an overdetermined consistent system of linear equations Ax=b by iteratively projecting onto the solution spaces.

Numerical Analysis

On Quantum Detection and the Square-Root Measurement

no code implementations31 May 2000 Yonina C. Eldar, G. David Forney Jr

In this paper we consider the problem of constructing measurements optimized to distinguish between a collection of possibly non-orthogonal quantum states.

Quantum Physics

Cannot find the paper you are looking for? You can Submit a new open access paper.