1.2k citations · 2.3k across the 64 of their papers we have counts for
22 papers · 1 filter
Bayesian Federated Learning over Wireless Networks
Seunghoon Lee, Chanho Park, Song-Nam Hong +2
Federated learning is a privacy-preserving and distributed training method using heterogeneous data sets stored at local devices. Federated learning over wireless networks requires…
Statistical model-based evaluation of neural networks
Sandipan Das, Prakash B. Gohain, Alireza M. Javid +2
Using a statistical model-based data generation, we develop an experimental setup for the evaluation of neural networks (NNs). The setup helps to benchmark a set of NNs vis-a-vis m…
Deep Networks for Direction-of-Arrival Estimation in Low SNR
Georgios K. Papageorgiou, Mathini Sellathurai, Yonina C. Eldar
In this work, we consider direction-of-arrival (DoA) estimation in the presence of extreme noise using Deep Learning (DL). In particular, we introduce a Convolutional Neural Networ…
Point of Care Image Analysis for COVID-19
Daniel Yaron, Daphna Keidar, Elisha Goldstein +22
Early detection of COVID-19 is key in containing the pandemic. Disease detection and evaluation based on imaging is fast and cheap and therefore plays an important role in COVID-19…
FlowStep3D: Model Unrolling for Self-Supervised Scene Flow Estimation
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. Traditional learning-based methods designed to learn end-to-end 3D flow of…
FedRec: Federated Learning of Universal Receivers over Fading Channels
Mahdi Boloursaz Mashhadi, Nir Shlezinger, Yonina C. Eldar +1
Wireless communications is often subject to channel fading. Various statistical models have been proposed to capture the inherent randomness in fading, and conventional model-based…