Yonina C. Eldar

research

∙ 07/04/2022

BiTAT: Neural Network Binarization with Task-dependent Aggregated Transformation

Neural network quantization aims to transform high-precision weights and...

Geon Park, et al. ∙

research

∙ 07/03/2022

Task-Oriented Sensing, Computation, and Communication Integration for Multi-Device Edge AI

This paper studies a new multi-device edge artificial-intelligent (AI) s...

Dingzhu Wen, et al. ∙

research

∙ 06/29/2022

Theoretical Perspectives on Deep Learning Methods in Inverse Problems

In recent years, there have been significant advances in the use of deep...

Jonathan Scarlett, et al. ∙

research

∙ 06/15/2022

Beamforming in Integrated Sensing and Communication Systems with Reconfigurable Intelligent Surfaces

We consider transmit beamforming and reflection pattern design in reconf...

R. S. Prasobh Sankar, et al. ∙

research

∙ 06/08/2022

Channel Estimation with Hybrid Reconfigurable Intelligent Metasurfaces

Reconfigurable Intelligent Surfaces (RISs) are envisioned to play a key ...

Haiyang Zhang, et al. ∙

research

∙ 06/08/2022

Mixed-Timescale Deep-Unfolding for Joint Channel Estimation and Hybrid Beamforming

In massive multiple-input multiple-output (MIMO) systems, hybrid analog-...

Kai Kang, et al. ∙

research

∙ 06/03/2022

Federated Deep Learning Meets Autonomous Vehicle Perception: Design and Verification

Realizing human-like perception is a challenge in open driving scenarios...

Shuai Wang, et al. ∙

research

∙ 06/01/2022

Edge Learning for B5G Networks with Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensing

To process and transfer large amounts of data in emerging wireless servi...

Wei Xu, et al. ∙

research

∙ 05/31/2022

Collaborative Sensing in Perceptive Mobile Networks: Opportunities and Challenges

With the development of innovative applications that demand accurate env...

Lei Xie, et al. ∙

research

∙ 05/05/2022

Model-Based Deep Learning: On the Intersection of Deep Learning and Optimization

Decision making algorithms are used in a multitude of different applicat...

Nir Shlezinger, et al. ∙

research

∙ 04/23/2022

Intelligent Reflecting Surface Enabled Sensing: Cramér-Rao Lower Bound Optimization

This paper investigates intelligent reflecting surface (IRS) enabled non...

Xianxin Song, et al. ∙

research

∙ 04/09/2022

Ultrasound Signal Processing: From Models to Deep Learning

Medical ultrasound imaging relies heavily on high-quality signal process...

Ben Luijten, et al. ∙

research

∙ 04/08/2022

Capacity Bounds for One-Bit MIMO Gaussian Channels with Analog Combining

The use of 1-bit analog-to-digital converters (ADCs) is seen as a promis...

Neil Irwin Bernardo, et al. ∙

research

∙ 03/27/2022

Online Meta-Learning For Hybrid Model-Based Deep Receivers

Recent years have witnessed growing interest in the application of deep ...

Tomer Raviv, et al. ∙

research

∙ 02/10/2022

On the Acquisition of Stationary Signals Using Uniform ADCs

In this work, we consider the acquisition of stationary signals using un...

Peter Neuhaus, et al. ∙

research

∙ 12/03/2021

MIMO Networks with One-Bit ADCs: Receiver Design and Communication Strategies

High resolution analog to digital converters (ADCs) are conventionally u...

Abbas Khalili, et al. ∙

research

∙ 10/24/2021

Learning to Estimate Without Bias

We consider the use of deep learning for parameter estimation. We propos...

Tzvi Diskin, et al. ∙

research

∙ 10/22/2021

Two-Timescale End-to-End Learning for Channel Acquisition and Hybrid Precoding

In this paper, we propose an end-to-end deep learning-based joint transc...

Qiyu Hu, et al. ∙

research

∙ 10/20/2021

Robust lEarned Shrinkage-Thresholding (REST): Robust unrolling for sparse recover

In this paper, we consider deep neural networks for solving inverse prob...

Wei Pu, et al. ∙

research

∙ 10/18/2021

Unsupervised Learned Kalman Filtering

In this paper we adapt KalmanNet, which is a recently pro-posed deep neu...

Guy Revach, et al. ∙

research

∙ 10/10/2021

Uncertainty in Data-Driven Kalman Filtering for Partially Known State-Space Models

Providing a metric of uncertainty alongside a state estimate is often cr...

Itzik Klein, et al. ∙

research

∙ 10/05/2021

Time-Based Quantization for FRI and Bandlimited signals

We consider the problem of quantizing samples of finite-rate-of-innovati...

Hila Naaman, et al. ∙

research

∙ 09/28/2021

Deep Unrolled Recovery in Sparse Biological Imaging

Deep algorithm unrolling has emerged as a powerful model-based approach ...

Yair Ben Sahel, et al. ∙

research

∙ 09/23/2021

Deep Learning for Ultrasound Beamforming

Diagnostic imaging plays a critical role in healthcare, serving as a fun...

Ruud JG van Sloun, et al. ∙

research

∙ 08/20/2021

deep unfolding for non-negative matrix factorization with application to mutational signature analysis

Non-negative matrix factorization (NMF) is a fundamental matrix decompos...

Rami Nasser, et al. ∙

research

∙ 08/15/2021

Deep Algorithm Unrolling for Biomedical Imaging

In this chapter, we review biomedical applications and breakthroughs via...

Yuelong Li, et al. ∙

research

∙ 07/21/2021

KalmanNet: Neural Network Aided Kalman Filtering for Partially Known Dynamics

Real-time state estimation of dynamical systems is a fundamental task in...

Guy Revach, et al. ∙

research

∙ 07/12/2021

Learned super resolution ultrasound for improved breast lesion characterization

Breast cancer is the most common malignancy in women. Mammographic findi...

Or Bar-Shira, et al. ∙

research

∙ 06/30/2021

Graph Signal Restoration Using Nested Deep Algorithm Unrolling

Graph signal processing is a ubiquitous task in many applications such a...

Masatoshi Nagahama, et al. ∙

research

∙ 06/10/2021

FRI-TEM: Time Encoding Sampling of Finite-Rate-of-Innovation Signals

Classical sampling is based on acquiring signal amplitudes at specific p...

Hila Naaman, et al. ∙

research

∙ 04/10/2021

Hybrid Reconfigurable Intelligent Metasurfaces: Enabling Simultaneous Tunable Reflections and Sensing for 6G Wireless Communications

Current discussions on the sixth Generation (6G) of wireless communicati...

George C. Alexandropoulos, et al. ∙

research

∙ 03/31/2021

Federated Learning: A Signal Processing Perspective

The dramatic success of deep learning is largely due to the availability...

Tomer Gafni, et al. ∙

research

∙ 03/24/2021

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

Deep neural networks (DNNs) based digital receivers can potentially oper...

Tomer Raviv, et al. ∙

research

∙ 03/01/2021

Deep Unfolded Recovery of Sub-Nyquist Sampled Ultrasound Image

The most common technique for generating B-mode ultrasound (US) images i...

Alon Mamistvalov, et al. ∙

research

∙ 02/08/2021

Adaptive Quantization of Model Updates for Communication-Efficient Federated Learning

Communication of model updates between client nodes and the central aggr...

Divyansh Jhunjhunwala, et al. ∙

research

∙ 02/05/2021

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

The need to recover high-dimensional signals from their noisy low-resolu...

Shahin Khobahi, et al. ∙

research

∙ 02/04/2021

Image Restoration by Deep Projected GSURE

Ill-posed inverse problems appear in many image processing applications,...

Shady Abu Hussein, et al. ∙

research

∙ 01/26/2021

A Coding Theory Perspective on Multiplexed Molecular Profiling of Biological Tissues

High-throughput and quantitative experimental technologies are experienc...

Luca D'Alessio, et al. ∙

research

∙ 01/12/2021

Model-Based Machine Learning for Communications

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

Nir Shlezinger, et al. ∙

research

∙ 12/31/2020

Bayesian Federated Learning over Wireless Networks

Federated learning is a privacy-preserving and distributed training meth...

Seunghoon Lee, et al. ∙

research

∙ 12/15/2020

Model-Based Deep Learning

Signal processing, communications, and control have traditionally relied...

Nir Shlezinger, et al. ∙

research

∙ 11/19/2020

FlowStep3D: Model Unrolling for Self-Supervised Scene Flow Estimation

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

Yair Kittenplon, et al. ∙

research

∙ 11/18/2020

Statistical model-based evaluation of neural networks

Using a statistical model-based data generation, we develop an experimen...

Sandipan Das, et al. ∙

research

∙ 11/17/2020

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

In this work, we consider direction-of-arrival (DoA) estimation in the p...

Georgios K. Papageorgiou, et al. ∙

research

∙ 11/14/2020

FedRec: Federated Learning of Universal Receivers over Fading Channels

Wireless communications are often subject to fading conditions. Various ...

Mahdi Boloursaz Mashhadi, et al. ∙

research

∙ 10/28/2020

Point of Care Image Analysis for COVID-19

Early detection of COVID-19 is key in containing the pandemic. Disease d...

Daniel Yaron, et al. ∙

research

∙ 10/22/2020

Unfolding Neural Networks for Compressive Multichannel Blind Deconvolution

We propose a learned-structured unfolding neural network for the problem...

Bahareh Tolooshams, et al. ∙

research

∙ 10/21/2020

Unrolling of Deep Graph Total Variation for Image Denoising

While deep learning (DL) architectures like convolutional neural network...

Huy Vu, et al. ∙

research

∙ 10/14/2020

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...

Nicolae-Catalin Ristea, et al. ∙

research

∙ 10/12/2020

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

One of the main challenges in containing the Coronoavirus disease 2019 (...

Alejandro Cohen, et al. ∙

Yonina C. Eldar

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