AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Modern applications of AI rely on increasingly complex models. Explainable AI (XAI) has emerged as a set of techniques aimed at improving model transparency. However, existing XAI methods typically assume input features to be inherently interpretable, or they rely on intermediate internal abstractions that are difficul...
This work proposes LRP-radial-all for origin-centered radial modules, treating geometric scaling as modulation and assigning relevance entirely to the signal branch, which achieves competitive attribution fidelity across datasets with runtime comparable to Gradient and substantially lower than Integrated Gradients.
Chronicle is introduced, an explainable transformer that learns from entire patient trajectories, predicts diverse clinical outcomes throughout the disease course while capturing both short- and long-term temporal dependencies, providing a scalable framework to support individualized treatment decisions.
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