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 ...
Abstract: Information aggregation and propagation over networks via graph neural networks (GNNs) plays an important role in node or graph representation learning, which currently depend on the ...
Ever wonder why ChatGPT slows down during long conversations? The culprit is a fundamental mathematical challenge: Processing long sequences of text requires massive computational resources, even with ...
Spline graphs connecting sparse data points create smooth curves that mislead users into believing there's continuous data when there are actually large gaps between measurements. This can cause ...
Knowledge graphs (KGs) are the foundation of artificial intelligence applications but are incomplete and sparse, affecting their effectiveness. Well-established KGs such as DBpedia and Wikidata lack ...
High sparse Knowledge Graph is a key challenge to solve the Knowledge Graph Completion task. Due to the sparsity of the KGs, there are not enough first-order neighbors to learn the features of ...
Abstract: This paper investigates the recovery of a node-domain sparse graph signal from the output of a graph filter. This problem, which is often referred to as the identification of the source of a ...
While it is important to find the key biomarkers and improve the accuracy of disease models, it is equally important to understand their interaction relationships. In this study, a transparent sparse ...
Official implementation of the paper "Understanding Sparse Neural Networks from their Topology via Multipartite Graph Representations", TMLR, April 2024. Authors: Elia Cunegatti, Matteo Farina, Doina ...
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