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Sponsored AI Networking Cookbook: Practical recipes for AI-assisted network automation and development AI Networking Cookbook: Practical recipes for AI-assisted network automation and development

Author

Wen-Kai Lu

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Book Open access Aug 2026

Capturing Motif Topological Diversity via Geometry-Adaptive Riemannian Molecular Representation Learning

A geometry-adaptive Riemannian framework for molecular representation learning, which explicitly models motifs as the basic units and learns their embeddings across multiple constant-curvature spaces, offering a general and scalable framework for scientific molecular modeling.

Fei Liu, Wen-Kai Lu, Fei-Long Wang et al. · 0 citations
Book Open access Aug 2026

Capturing Motif Topological Diversity via Geometry-Adaptive Riemannian Molecular Representation Learning

Molecular properties are often governed by a small number of local substructures, or motifs, whose topologies can vary drastically across molecules. Existing molecular representation learning approaches typically embed all motifs into a single Euclidean or fixed-curvature space, which fails to capture the motif-level t...

Fei Liu, Wen-Kai Lu, Feilong Wang et al. · 0 citations

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