1 citations · 2 across the 15 of their papers we have counts for
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EM-KalmanNet: Learned Expectation-Maximization for Adaptive Tracking in Partially Known, Block-Wise Time-Varying State-Space Models
Ori Cohen, Nir Shlezinger, Tirza Routtenberg
State estimation in partially known state space (SS) models is challenging when the dynamics or observation model varies across short data blocks. Classical model-based approaches,…
Beam Focusing for Near-Field Multi-User Localization
Qianyu Yang, Anna Guerra, Francesco Guidi +5
Extremely large-scale antenna arrays are poised to play a pivotal role in sixth-generation (6G) networks. Utilizing such arrays often results in a near-field spherical wave transmi…
Model-Based Deep Learning
Nir Shlezinger, Yonina C. Eldar
Signal processing traditionally relies on classical statistical modeling techniques. Such model-based methods utilize mathematical formulations that represent the underlying physic…
Latent-KalmanNet: Learned Kalman Filtering for Tracking from High-Dimensional Signals
Itay Buchnik, Damiano Steger, Guy Revach +3
The Kalman filter (KF) is a widely-used algorithm for tracking dynamic systems that are captured by state space (SS) models. The need to fully describe a SS model limits its applic…
Collaborative Inference for AI-Empowered IoT Devices
Nir Shlezinger, Ivan V. Bajic
Artificial intelligence (AI) technologies, and particularly deep learning systems, are traditionally the domain of large-scale cloud servers, which have access to high computationa…