Semi-Supervised Learning of Multi-Scale Graph Neural Networks for Industrial Process Fault Diagnosis
Abstract: The scarcity of labeled data is a critical challenge in industrial process multi-scale modeling, as learning reliable models from limited labeled data and large-scale unlabeled data is ...
A new technical paper titled “A Case for Hypergraphs to Model and Map SNNs on Neuromorphic Hardware” was published by researchers at Politecnico di Milano. “Executing Spiking Neural Networks (SNNs) on ...
ABSTRACT: Foot-and-Mouth Disease (FMD) remains a critical threat to global livestock industries, causing severe economic losses and trade restrictions. This paper proposes a novel application of ...
A new technical paper titled “Hardware Acceleration for Neural Networks: A Comprehensive Survey” was published by researchers at Arizona State University. “Neural networks have become a dominant ...
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 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 ...
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 ...
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