4 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…
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…
Spectrum Breathing: Protecting Over-the-Air Federated Learning Against Interference
Zhanwei Wang, Kaibin Huang, Yonina C. Eldar
Federated Learning (FL) is a widely embraced paradigm for distilling artificial intelligence from distributed mobile data. However, the deployment of FL in mobile networks can be c…