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Organizations have a wealth of unstructured data that most AI models can’t yet read. Preparing and contextualizing this data is essential for moving from AI experiments to measurable results.
Chinese and Singaporean researchers have developed a defense mechanism that poisons proprietary knowledge graph data, making ...
RAG, on the other hand, exists because many real questions depend on current, specific, or proprietary data (such as company ...
Euronet Worldwide has been operating ATMs internationally for around three decades, and currently generates 37% margins from ...
For hackers, the stolen data would be useless, but authorized users would have a secret key that filters out the fake ...
Adapting to the Stream: An Instance-Attention GNN Method for Irregular Multivariate Time Series Data
DynIMTS replaces static graphs with instance-attention that updates edge weights on the fly, delivering SOTA imputation and P12 classification ...
OpenX's new API suite provides ad buyers with a more detailed view of the bidstream and, as a result, more accurate targeting ...
Daimler Truck uses graph technology to untangle its IT estate and gains long-lasting operational windfall - SiliconANGLE ...
Knowledge graphs are a powerful tool for bringing together information from biological databases and linking what is already ...
Knowledge graphs are a powerful tool for bringing together information from biological databases and linking what is already ...
Abstract: Burgeoning graph contrastive learning (GCL) stands out in the graph domain with low annotated costs and high model performance improvements, which is typically composed of three standard ...
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