Despite significant mathematical refinements, econometrics has shown the weaknesses of its logical underpinnings, primarily during economic turning points—financial crises, pandemics, and geopolitical ...
Abstract: Modern mechanical equipment increasingly relies on multi-sensor data for health monitoring, which generates complex, heterogeneous, non-Euclidean data that conventional methods struggle to ...
Abstract: Anomaly detection in cloud computing environments is increasingly challenging due to the complexity of distributed architectures, the heterogeneity of infrastructure components, and the high ...
This repository provides the official PyTorch implementation for CHAMP (Coupled Hierarchical Atom-Motif Predictor), a novel hierarchical Graph Neural Network framework designed to achieve state-of-the ...
Cloud infrastructure anomalies cause significant downtime and financial losses (estimated at $2.5 M/hour for major services). Traditional anomaly detection methods fail to capture complex dependencies ...
CHAMP: A Coupled Hierarchical Atom-Motif Predictor This repository provides the official PyTorch implementation for CHAMP (Coupled Hierarchical Atom-Motif Predictor), a novel hierarchical Graph Neural ...
Graph Neural Networks (GNNs) and GraphRAG don’t “reason”—they navigate complex, open-world financial graphs with traceable, multi-hop evidence. Here’s why BFSI leaders should embrace graph-native AI ...
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