Abstract: In recent years, graph convolutional networks (GCNs) have been introduced for hyperspectral image (HSI) classification due to their ability to effectively process the inherent graph ...
Graphs are a ubiquitous data structure and a universal language for representing objects and complex interactions. They can model a wide range of real-world systems, such as social networks, chemical ...
A new technical paper titled “Hardware-Aware Fine-Tuning of Spiking Q-Networks on the SpiNNaker2 Neuromorphic Platform” was published by researchers at TU Dresden, ScaDS.AI and Centre for Tactile ...
A new study led by researchers from the Yunnan Observatories of the Chinese Academy of Sciences has developed a neural network-based method for large-scale celestial object classification, according ...
A new technical paper titled “Machine Intelligence on Wireless Edge Networks” was published by researchers at MIT and Duke University. “Deep neural network (DNN) inference on power-constrained edge ...
ABSTRACT: The rapid advancements in large language models (LLMs) have led to an exponential increase in survey papers, making it challenging to systematically track and analyze their evolving taxonomy ...
1 Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran 2 College of Engineering, Design and Physical Sciences, Brunel University London, Uxbridge, United Kingdom ...
Abstract: Compared with traditional neural networks, graph convolutional networks are very suitable for processing graph structured data. However, common graph convolutional network methods often have ...
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