Cognitive computing represents an innovative frontier within computer science, merging artificial intelligence, machine ...
ABSTRACT: This study proposes a dual-architecture Explainable Artificial Intelligence (XAI) framework designed to unify risk scoring methodologies across corporate and retail lending domains. The ...
Abstract: Hypergraph representation learning (HGRL) has attracted widespread attention for its ability to capture high-order relationships (simultaneous interactions among multiple entities) in ...
Background: Molecular interactions are central to numerous challenges in chemistry and the life sciences. Whether in solute–solvent dissolution, adverse drug–drug interactions, or protein complex ...
We collaborate with the world's leading lawyers to deliver news tailored for you. Sign Up for any (or all) of our 25+ Newsletters. Some states have laws and ethical rules regarding solicitation and ...
Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Vivek Yadav, an engineering manager from ...
The key to enterprise-wide AI adoption is trust. Without transparency and explainability, organizations will find it difficult to implement success-driven AI initiatives. Interpretability doesn’t just ...
ABSTRACT: Machine learning (ML) has revolutionized risk management by enabling organizations to make data-driven decisions with higher accuracy and speed. However, as machine learning models grow more ...
Although contrastive learning has been widely applied in hypergraph representation learning, most existing hypergraph contrastive learning methods still rely on random data augmentation schemes, such ...
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