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 ...

You’ve heard the maxim, “Trust, but verify.” That’s a contradiction—if you need to verify something, you don’t truly trust it. And if you can verify it, you probably don’t need trust at all! While ...

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. Cory Benfield discusses the evolution of ...

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 ...

As enterprises shift from AI experimentation to scaled implementation, one principle will separate hype from impact: explainability. This evolution requires implementing 'responsible AI' frameworks ...

Scientists usually use a hypergraph model to predict dynamic behaviors. But the opposite problem is interesting, too. What if researchers can observe the dynamics but don't have access to a reliable ...