Frontiers in Neurorobotics has published a new paper by Xiaofei Han and Xin Dou introducing a next-generation artificial intelligence framework that combines graph neural networks and multimodal ...
The research aim is to develop an intelligent agent for cybersecurity systems capable of detecting abnormal user behavior ...
Abstract: In this paper, we propose a robust end-to-end classification model, Graph-in-Graph Neural Network (GIGNet), for automatic modulation recognition (AMR). In GIGNet, multi-level graph neural ...
Neurological disorders, such as schizophrenia and bipolar disorder, remain challenging to diagnose due to the absence of objective biomarkers. Current assessments largely rely on subjective clinical ...
The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jcim.5c01525. Efficiency analysis of different normalization strategies ...
The recent past has seen an increasing interest in Heterogeneous Graph Neural Networks (HGNNs), since many real-world graphs are heterogeneous in nature, from citation graphs to email graphs. However, ...
Learn about the Network in Network architecture and its impact on improving performance in deep neural networks using PyTorch. ‘Slap in the Face’: Court Ruling on Gun Law Sparks Fury Mom Worried If ...
Abstract: Missing node attributes pose a common problem in real-world graphs, impacting the performance of graph neural networks’ representation learning. Existing GNNs often struggle to effectively ...
ABSTRACT: Knowledge Graph (KG) and neural network (NN) based Question-answering (QA) systems have evolved into the realm of intelligent information retrieval as they have been able to reach a high ...
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