From the 2 of 37 linked papers with an AI index.
7 papers · 1 filter
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…
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…
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…
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…