595 citations · 3.6k across the 131 of their papers we have counts for
16 papers · 1 filter
MANET-GNN: Learned Decentralized Optimization of Power Allocation in Multi-Channel MANETs
Tomer Alter, Nir Shlezinger, Michael Segal
MANETs enable flexible infrastructure-less wireless connectivity in dynamic and resource-constrained environments. As modern MANETs exploit multiple frequency channels and support…
Online Learning via Learned Latent Bayesian Tracking
Guy Gerson, Tomer Raviv, Nir Shlezinger +2
Online learning in non-stationary environments requires models to adapt rapidly from streaming data under strict computational constraints. A principled approach casts online learn…
SGD-Based Knowledge Distillation with Bayesian Teachers: Theory and Guidelines
Itai Morad, Nir Shlezinger, Yonina C. Eldar
Knowledge Distillation (KD) is a central paradigm for transferring knowledge from a large teacher network to a typically smaller student model, often by leveraging soft probabilist…
Deep Unfolding: Recent Developments, Theory, and Design Guidelines
Nir Shlezinger, Santiago Segarra, Yi Zhang +4
Optimization methods play a central role in signal processing, serving as the mathematical foundation for inference, estimation, and control. While classical iterative optimization…
Adaptive Deadline and Batch Layered Synchronized Federated Learning
Asaf Goren, Natalie Lang, Nir Shlezinger +1
Federated learning (FL) enables collaborative model training across distributed edge devices while preserving data privacy, and typically operates in a round-based synchronous mann…
Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers
Elad Sofer, Tomer Shaked, Caroline Chaux +1
Machine learning (ML) models are often sensitive to carefully crafted yet seemingly unnoticeable perturbations. Such adversarial examples are considered to be a property of ML mode…
PAUSE: Low-Latency and Privacy-Aware Active User Selection for Federated Learning
Ori Peleg, Natalie Lang, Dan Ben Ami +3
Federated learning (FL) enables multiple edge devices to collaboratively train a machine learning model without the need to share potentially private data. Federated learning proce…
Deep Variational Sequential Monte Carlo for High-Dimensional Observations
Wessel L. van Nierop, Nir Shlezinger, Ruud J. G. van Sloun
Sequential Monte Carlo (SMC), or particle filtering, is widely used in nonlinear state-space systems, but its performance often suffers from poorly approximated proposal and state-…
AI-Aided Kalman Filters
Nir Shlezinger, Guy Revach, Anubhab Ghosh +7
The Kalman filter (KF) and its variants are among the most celebrated algorithms in signal processing. These methods are used for state estimation of dynamic systems by relying on…
Stragglers-Aware Low-Latency Synchronous Federated Learning via Layer-Wise Model Updates
Natalie Lang, Alejandro Cohen, Nir Shlezinger
Synchronous federated learning (FL) is a popular paradigm for collaborative edge learning. It typically involves a set of heterogeneous devices locally training neural network (NN)…
Joint Privacy Enhancement and Quantization in Federated Learning
Natalie Lang, Elad Sofer, Tomer Shaked +1
Federated learning (FL) is an emerging paradigm for training machine learning models using possibly private data available at edge devices. The distributed operation of FL gives ri…
Decentralized Low-Latency Collaborative Inference via Ensembles on the Edge
May Malka, Erez Farhan, Hai Morgenstern +1
The success of deep neural networks (DNNs) is heavily dependent on computational resources. While DNNs are often employed on cloud servers, there is a growing need to operate DNNs…
Over-the-Air Federated Learning from Heterogeneous Data
Tomer Sery, Nir Shlezinger, Kobi Cohen +1
Federated learning (FL) is a framework for distributed learning of centralized models. In FL, a set of edge devices train a model using their local data, while repeatedly exchangin…
UVeQFed: Universal Vector Quantization for Federated Learning
Nir Shlezinger, Mingzhe Chen, Yonina C. Eldar +2
Traditional deep learning models are trained at a centralized server using labeled data samples collected from end devices or users. Such data samples often include private informa…
Learned Factor Graphs for Inference from Stationary Time Sequences
Nir Shlezinger, Nariman Farsad, Yonina C. Eldar +1
The design of methods for inference from time sequences has traditionally relied on statistical models that describe the relation between a latent desired sequence and the observed…
ViterbiNet: A Deep Learning Based Viterbi Algorithm for Symbol Detection
Nir Shlezinger, Nariman Farsad, Yonina C. Eldar +1
Symbol detection plays an important role in the implementation of digital receivers. In this work, we propose ViterbiNet, which is a data-driven symbol detector that does not requi…