Learning a graph-based classifier for fault localization
Sign Up to like & getrecommendations! Published in 2020 at "Science China Information Sciences"
DOI: 10.1007/s11432-019-2720-1
Abstract: Because software emerged, locating software faults has been intensively researched, culminating in various approaches and tools that have been applied in real development. Despite the success of these developments, improved tools are still demanded by… read more here.
Keywords: bug fixes; graph based; code; bug ... See more keywords
Graph Geometric Algebra networks for graph representation learning
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DOI: 10.1038/s41598-024-84483-0
Abstract: Graph neural networks (GNNs) have emerged as a prominent approach for capturing graph topology and modeling vertex-to-vertex relationships. They have been widely used in pattern recognition tasks including node and graph label prediction. However, when… read more here.
Keywords: graph geometric; networks graph; algebra; learning graph ... See more keywords
MAGUS: machine learning and graph theory assisted universal structure searcher
Sign Up to like & getrecommendations! Published in 2023 at "National Science Review"
DOI: 10.1093/nsr/nwad128
Abstract: Crystal structure predictions based on first-principles calculations have gained great success in materials science and solid state physics. However, the remaining challenges still limit their applications in systems with a large number of atoms, especially… read more here.
Keywords: learning graph; machine learning; structure; graph theory ... See more keywords
Deep Reinforcement Learning With Graph Representation for Vehicle Repositioning
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Intelligent Transportation Systems"
DOI: 10.1109/tits.2021.3119662
Abstract: On-demand ride services, e.g., taxi companies, ridesourcing/transportation network companies (TNCs), play a vital role in nowadays transportation modes. The relocation/repositioning of idle vehicles is an important operational problem for service providers. With the recent success… read more here.
Keywords: problem; reinforcement learning; deep reinforcement; transportation ... See more keywords
Deep Learning with Graph Convolutional Networks: An Overview and Latest Applications in Computational Intelligence
Sign Up to like & getrecommendations! Published in 2023 at "International Journal of Intelligent Systems"
DOI: 10.1155/2023/8342104
Abstract: Convolutional neural networks (CNNs) have received widespread attention due to their powerful modeling capabilities and have been successfully applied in natural language processing, image recognition, and other fields. On the other hand, traditional CNN can… read more here.
Keywords: graph convolutional; intelligence; convolutional networks; graph ... See more keywords
Machine Learning and Graph Signal Processing Applied to Healthcare: A Review
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DOI: 10.3390/bioengineering11070671
Abstract: Signal processing is a very useful field of study in the interpretation of signals in many everyday applications. In the case of applications with time-varying signals, one possibility is to consider them as graphs, so… read more here.
Keywords: learning graph; graph signal; signal processing; machine learning ... See more keywords