GRAPHLCP improves localized conformal prediction on graphs by using feature-aware densification and Personalized PageRank kernels to incorporate topology for better coverage and efficiency.
Schoenholz, Patrick F
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GHR uses hierarchical recurrence on pooled graph abstractions to improve long-range dependency capture and out-of-range generalization while using far fewer parameters than existing models.
A systematic survey and benchmark of four deep learning paradigms for molecular property prediction that organizes the field, critiques current data practices, and outlines three future directions.
citing papers explorer
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GRAPHLCP: Structure-Aware Localized Conformal Prediction on Graphs
GRAPHLCP improves localized conformal prediction on graphs by using feature-aware densification and Personalized PageRank kernels to incorporate topology for better coverage and efficiency.
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Graph Hierarchical Recurrence for Long-Range Generalization
GHR uses hierarchical recurrence on pooled graph abstractions to improve long-range dependency capture and out-of-range generalization while using far fewer parameters than existing models.
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A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era
A systematic survey and benchmark of four deep learning paradigms for molecular property prediction that organizes the field, critiques current data practices, and outlines three future directions.
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