SGR turns adversarial shortcuts into teachers, isolating invariant subgraph rationales and delivering the first XGL framework ...
Like all epidemics, the COVID-19 pandemic escalates in a crisis of individual and collective character. While in some ways crises bring people together, they also expose and stress systemic flaws and ...
Graph matching remains a core challenge in computer vision, where establishing correspondences between features is crucial for tasks such as object recognition, 3D reconstruction and scene ...
Graph labeling is a central topic in combinatorial optimisation that involves assigning numerical or categorical labels to vertices or edges of a graph subject to specific constraints. This framework ...
The analysis of cancer biology data involves extremely heterogeneous data sets, including information from RNA sequencing, genome-wide copy number, DNA methylation data reporting on epigenetic ...
No-code Graph RAG employs autonomous agents to integrate enterprise data and domain knowledge with LLMs for context-rich, explainable conversations Graphwise, a leading Graph AI provider, announced ...
A new research paper titled “Equivalence Checking of System-Level and SPICE-Level Models of Linear Circuits” was published by researchers at University of Bremen and DFKI GmbH. “Due to the increasing ...
A super geeky topic, which could have super important repercussions in the real world. That description could very well fit anything from cold fusion to knowledge graphs, so a bit of unpacking is in ...
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