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Graph neural networks (GNNs) have gained traction and have been applied to various graph-based data analysis tasks due to their high performance. However, a major concern is their robustness, ...
Alongside text-based large language models (LLMs), including ChatGPT in industrial fields, GNN (Graph Neural Network)-based graph AI models that analyze unstructured data such as financial ...
Representing a molecule in a way that captures both its structure and function is central to tasks such as molecular property prediction, drug drug ...
As one of the most crucial topics in the recommendation system field, Point-of-Interest (POI) recommendation aims to recommending potential interesting POIs to users. Recently, graph neural networks ...
MicroCloud Hologram Inc. (NASDAQ: HOLO), ("HOLO" or the "Company"), a technology service provider, released learnable quantum spectral filter technology for hybrid graph neural networks. This ...
A new technical paper titled “TROJAN-GUARD: Hardware Trojans Detection Using GNN in RTL Designs” was published by researchers at University of Connecticut and University of Minnesota. “hip ...
A technical paper titled “SCAR: Power Side-Channel Analysis at RTL-Level” was published by researchers at University of Texas at Dallas, Technology Innovation Institute and University of Illinois ...
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