Yonina C. Eldar

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Technion ∙ Weizmann Institute of Science

Professor of Electrical Engineering at Technion - Israel Institute of Technology, Adjunt Professor at Duke University

Federated Learning: A Signal Processing Perspective

The dramatic success of deep learning is largely due to the availability...
03/31/2021 ∙ by Tomer Gafni, et al. ∙ 0 ∙ share

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Meta-ViterbiNet: Online Meta-Learned Viterbi Equalization for Non-Stationary Channels

Deep neural networks (DNNs) based digital receivers can potentially oper...
03/24/2021 ∙ by Tomer Raviv, et al. ∙ 0 ∙ share

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Deep Unfolded Recovery of Sub-Nyquist Sampled Ultrasound Image

The most common technique for generating B-mode ultrasound (US) images i...
03/01/2021 ∙ by Alon Mamistvalov, et al. ∙ 0 ∙ share

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Adaptive Quantization of Model Updates for Communication-Efficient Federated Learning

Communication of model updates between client nodes and the central aggr...
02/08/2021 ∙ by Divyansh Jhunjhunwala, et al. ∙ 0 ∙ share

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

The need to recover high-dimensional signals from their noisy low-resolu...
02/05/2021 ∙ by Shahin Khobahi, et al. ∙ 0 ∙ share

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Image Restoration by Deep Projected GSURE

Ill-posed inverse problems appear in many image processing applications,...
02/04/2021 ∙ by Shady Abu Hussein, et al. ∙ 10 ∙ share

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A Coding Theory Perspective on Multiplexed Molecular Profiling of Biological Tissues

High-throughput and quantitative experimental technologies are experienc...
01/26/2021 ∙ by Luca D'Alessio, et al. ∙ 0 ∙ share

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Model-Based Machine Learning for Communications

We present an introduction to model-based machine learning for communica...
01/12/2021 ∙ by Nir Shlezinger, et al. ∙ 0 ∙ share

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Bayesian Federated Learning over Wireless Networks

Federated learning is a privacy-preserving and distributed training meth...
12/31/2020 ∙ by Seunghoon Lee, et al. ∙ 0 ∙ share

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Model-Based Deep Learning

Signal processing, communications, and control have traditionally relied...
12/15/2020 ∙ by Nir Shlezinger, et al. ∙ 86 ∙ share

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FlowStep3D: Model Unrolling for Self-Supervised Scene Flow Estimation

Estimating the 3D motion of points in a scene, known as scene flow, is a...
11/19/2020 ∙ by Yair Kittenplon, et al. ∙ 0 ∙ share

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Statistical model-based evaluation of neural networks

Using a statistical model-based data generation, we develop an experimen...
11/18/2020 ∙ by Sandipan Das, et al. ∙ 0 ∙ share

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

In this work, we consider direction-of-arrival (DoA) estimation in the p...
11/17/2020 ∙ by Georgios K. Papageorgiou, et al. ∙ 0 ∙ share

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FedRec: Federated Learning of Universal Receivers over Fading Channels

Wireless communications are often subject to fading conditions. Various ...
11/14/2020 ∙ by Mahdi Boloursaz Mashhadi, et al. ∙ 0 ∙ share

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Point of Care Image Analysis for COVID-19

Early detection of COVID-19 is key in containing the pandemic. Disease d...
10/28/2020 ∙ by Daniel Yaron, et al. ∙ 0 ∙ share

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Unfolding Neural Networks for Compressive Multichannel Blind Deconvolution

We propose a learned-structured unfolding neural network for the problem...
10/22/2020 ∙ by Bahareh Tolooshams, et al. ∙ 2 ∙ share

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Unrolling of Deep Graph Total Variation for Image Denoising

While deep learning (DL) architectures like convolutional neural network...
10/21/2020 ∙ by Huy Vu, et al. ∙ 0 ∙ share

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Automotive Radar Interference Mitigation with Unfolded Robust PCA based on Residual Overcomplete Auto-Encoder Blocks

Deep learning methods for automotive radar interference mitigation can s...
10/14/2020 ∙ by Nicolae-Catalin Ristea, et al. ∙ 0 ∙ share

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Multi-Level Group Testing with Application to One-Shot Pooled COVID-19 Tests

One of the main challenges in containing the Coronoavirus disease 2019 (...
10/12/2020 ∙ by Alejandro Cohen, et al. ∙ 0 ∙ share

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COVID-19 Classification of X-ray Images Using Deep Neural Networks

In the midst of the coronavirus disease 2019 (COVID-19) outbreak, chest ...
10/03/2020 ∙ by Elisha Goldstein, et al. ∙ 0 ∙ share

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

Deep unfolded neural networks are designed by unrolling the iterations o...
10/02/2020 ∙ by Huynh Van Luong, et al. ∙ 0 ∙ share

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Task-Based Analog-to-Digital Converters

Obtaining digital representations of multivariate continuous-time (CT) s...
09/29/2020 ∙ by Peter Neuhaus, et al. ∙ 0 ∙ share

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Over-the-Air Federated Learning from Heterogeneous Data

Federated learning (FL) is a framework for distributed learning of centr...
09/27/2020 ∙ by Tomer Sery, et al. ∙ 0 ∙ share

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On the Error Exponent of Approximate Sufficient Statistics for M-ary Hypothesis Testing

Consider the problem of detecting one of M i.i.d. Gaussian signals corru...
08/18/2020 ∙ by Jiachun Pan, et al. ∙ 0 ∙ share

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eSampling: Energy Harvesting ADCs

Analog-to-digital converters (ADCs) allow physical signals to be process...
07/16/2020 ∙ by Neha Jain, et al. ∙ 0 ∙ share

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Dynamic Metasurface Antennas for 6G Extreme Massive MIMO Communications

Next generation wireless base stations and access points will transmit a...
06/14/2020 ∙ by Nir Shlezinger, et al. ∙ 0 ∙ share

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UVeQFed: Universal Vector Quantization for Federated Learning

Traditional deep learning models are trained at a centralized server usi...
06/05/2020 ∙ by Nir Shlezinger, et al. ∙ 0 ∙ share

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Inference from Stationary Time Sequences via Learned Factor Graphs

The design of methods for inference from time sequences has traditionall...
06/05/2020 ∙ by Nir Shlezinger, et al. ∙ 0 ∙ share

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Ensemble Wrapper Subsampling for Deep Modulation Classification

Subsampling of received wireless signals is important for relaxing hardw...
05/10/2020 ∙ by Sharan Ramjee, et al. ∙ 6 ∙ share

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RaSSteR: Random Sparse Step-Frequency Radar

We propose a method for synthesizing high range resolution profiles (HRR...
04/12/2020 ∙ by Kumar Vijay Mishra, et al. ∙ 0 ∙ share

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Sampling on Graphs: From Theory to Applications

The study of sampling signals on graphs, with the goal of building an an...
03/09/2020 ∙ by Yuichi Tanaka, et al. ∙ 0 ∙ share

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Data-Driven Symbol Detection via Model-Based Machine Learning

The design of symbol detectors in digital communication systems has trad...
02/14/2020 ∙ by Nariman Farsad, et al. ∙ 0 ∙ share

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Task-Based Quantization with Application to MIMO Receivers

Multiple-input multiple-output (MIMO) systems are required to communicat...
02/11/2020 ∙ by Nir Shlezinger, et al. ∙ 0 ∙ share

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On Throughput of Millimeter Wave MIMO Systems with Low Resolution ADCs

Use of low resolution analog to digital converters (ADCs) is an effectiv...
02/11/2020 ∙ by Abbas Khalili, et al. ∙ 0 ∙ share

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DeepSIC: Deep Soft Interference Cancellation for Multiuser MIMO Detection

Digital receivers are required to recover the transmitted symbols from t...
02/08/2020 ∙ by Nir Shlezinger, et al. ∙ 0 ∙ share

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Data-Driven Factor Graphs for Deep Symbol Detection

Many important schemes in signal processing and communications, ranging ...
01/31/2020 ∙ by Nir Shlezinger, et al. ∙ 0 ∙ share

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Identifiability Conditions for Compressive Multichannel Blind Deconvolution

In applications such as multi-receiver radars and ultrasound array syste...
01/02/2020 ∙ by Satish Mulleti, et al. ∙ 0 ∙ share

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Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing

Deep neural networks provide unprecedented performance gains in many rea...
12/22/2019 ∙ by Vishal Monga, et al. ∙ 57 ∙ share

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Dynamic Metasurface Antennas for MIMO-OFDM Receivers with Bit-Limited ADCs

The combination of orthogonal frequency modulation (OFDM) and multiple-i...
12/14/2019 ∙ by Hanqing Wang, et al. ∙ 0 ∙ share

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Distributed Quantization for Sparse Time Sequences

Analog signals processed in digital hardware are quantized into a discre...
10/21/2019 ∙ by Alejandro Cohen, et al. ∙ 0 ∙ share

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MAJoRCom: A Dual-Function Radar Communication System Using Index Modulation

Dual-function radar communication (DFRC) systems implement both sensing ...
09/10/2019 ∙ by Tianyao Huang, et al. ∙ 0 ∙ share

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Joint Radar-Communications Strategies for Autonomous Vehicles

Self-driving cars constantly asses their environment in order to choose ...
09/04/2019 ∙ by Dingyou Ma, et al. ∙ 0 ∙ share

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Deep Task-Based Quantization

Quantizers play a critical role in digital signal processing systems. Re...
08/01/2019 ∙ by Nir Shlezinger, et al. ∙ 0 ∙ share

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Deep Neural Network Symbol Detection for Millimeter Wave Communications

This paper proposes to use a deep neural network (DNN)-based symbol dete...
07/25/2019 ∙ by Yun Liao, et al. ∙ 0 ∙ share

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Deep learning in ultrasound imaging

We consider deep learning strategies in ultrasound systems, from the fro...
07/05/2019 ∙ by Ruud JG van Sloun, et al. ∙ 0 ∙ share

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Serial Quantization for Representing Sparse Signals

Sparse signals are encountered in a broad range of applications. In orde...
07/03/2019 ∙ by Alejandro Cohen, et al. ∙ 0 ∙ share

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Multi-Carrier Agile Phased Array Radar

Modern radar systems are expected to operate reliably in congested envir...
06/14/2019 ∙ by Tianyao Huang, et al. ∙ 0 ∙ share

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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...
05/26/2019 ∙ by Nir Shlezinger, et al. ∙ 0 ∙ share

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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 ...
05/16/2019 ∙ by Xiwen Zhang, et al. ∙ 0 ∙ share

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Sample Efficient Toeplitz Covariance Estimation

We study the query complexity of estimating the covariance matrix T of a...
05/14/2019 ∙ by Yonina C. Eldar, et al. ∙ 0 ∙ share

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Yonina C. Eldar

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