From the 2 of 35 linked papers with an AI index.
35 papers
Change-Aware Self-Adaptive AI-Aided Kalman Filters With Neural Change Point Detection
Wenyi Zhang, Xiaoyong Ni, Nir Shlezinger +1
The paper proposes CASA‑KalmanNet, an online self‑adaptive Kalman filter that uses a neural change‑point detector to monitor internal reliability indicators and adjust learning whe…
DiffAU: Diffusion-Based Ambisonics Upscaling
Amit Milstein, Nir Shlezinger, Boaz Rafaely
The paper introduces DiffAU, a diffusion‑model‑based method that upscales first‑order Ambisonics recordings to third‑order Ambisonics, improving spatial resolution of 3D audio.
Bayesian KalmanNet: Quantifying Uncertainty in Deep Learning Augmented Kalman Filter
Yehonatan Dahan, Guy Revach, Jindrich Dunik +1
Recent years have witnessed a growing interest in tracking algorithms that augment Kalman Filters (KFs) with Deep Neural Networks (DNNs). By transforming KFs into trainable deep le…
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…
Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction
Ory Eger, Nir Shlezinger
The growing demands for higher throughput and cost-efficient wireless communications drive the need for receivers that are both simple to deploy and robust to hardware impairments…
Online Learning of Modular Bayesian Deep Receivers: Single-Step Adaptation with Streaming Data
Yakov Gusakov, Osvaldo Simeone, Tirza Routtenberg +1
Deep neural network (DNN)-based receivers offer a powerful alternative to classical model-based designs for wireless communication, especially in complex and nonlinear propagation…