Abstract: Recently, topological graphs based on structural or functional connectivity of brain network have been utilized to construct graph neural networks (GNN) for Electroencephalogram (EEG) ...
As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...
As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...
Learn how backpropagation works by building it from scratch in Python! This tutorial explains the math, logic, and coding behind training a neural network, helping you truly understand how deep ...
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, ...
Department of Materials Science and Engineering, Indian Institute of Technology, Delhi-110016, India Department of Chemistry, Indian Institute of Technology, Delhi-110016, India ...
STM-Graph is a Python framework for analyzing spatial-temporal urban data and doing predictions using Graph Neural Networks. It provides a complete end-to-end pipeline from raw event data to trained ...
With the recent popularity of neural networks comes the need for efficient serving of inference workloads. A neural network inference workload can be represented as a computational graph with nodes as ...
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 ...
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