Oct 22, 2020 · Abstract:Graph neural networks (GNNs) are popular to use for classifying structured data in the context of machine learning.
Graph neural networks (GNNs) are popular to use for classifying structured data in the context of machine learning. But surprisingly, they are rarely applied to ...
GNN-For-localization. "GRAPH NEURAL NETWORK FOR LARGE-SCALE NETWORK LOCALIZATION" https://arxiv.org/abs/2010.11653 (Accepted by ICASSP 2021). Requirments ...
Feb 14, 2024 · We first introduce a novel network localization method based on graph convolutional network (GCN), which exhibits exceptional pre- cision even ...
This work adopts GNN for a classic but challenging nonlinear regression problem, namely the network localization, and finds that GNN is potentially the best ...
Jun 22, 2021 · This approach leverages deep learning to train the channel propagation model during the offline phase and enables online determination of ...
Oct 30, 2020 · In this work, we adopt GNN for a classic but challenging nonlinear regression problem, namely the network localization. Our main findings are in ...
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Training GNNs on large graphs presents challenges that hinder the application of GNNs to large-scale problems in various domains. Intel Labs is developing open- ...
Missing: Localization. | Show results with:Localization.
Mar 21, 2023 · It offers a fast and easy-to-use tool for large network visualization. We find that, many large networks have informative large-scale ...
Mar 11, 2024 · Then, we introduce FloorLocator, a deep learning-based method for floor localization that integrates efficient spiking neural networks with ...