MolCHG uses a multi-level compositional hierarchical graph with atom-bond cross-view contrastive learning, functional group prediction, and structure tasks to achieve top results on seven of nine MoleculeNet benchmarks.
Silhouettes: a graphical aid to the interpretation and validation of cluster analysis.Journal of computational and applied mathematics, 20:53–65
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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PREFER is an online preference learning system that generates personalized review summaries and improves alignment with user interests in simulations on Amazon review data.
An unsupervised system-aware framework combines online detection with an LLM-augmented contextual digital twin to deliver real-time, interpretable anomaly diagnosis in industrial control systems.
citing papers explorer
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Multi-level Self-supervised Pretraining on Compositional Hierarchical Graph for Molecular Property Prediction
MolCHG uses a multi-level compositional hierarchical graph with atom-bond cross-view contrastive learning, functional group prediction, and structure tasks to achieve top results on seven of nine MoleculeNet benchmarks.
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PREFER: Personalized Review Summarization with Online Preference Learning
PREFER is an online preference learning system that generates personalized review summaries and improves alignment with user interests in simulations on Amazon review data.
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System-aware contextual digital twin for ICS anomaly diagnosis
An unsupervised system-aware framework combines online detection with an LLM-augmented contextual digital twin to deliver real-time, interpretable anomaly diagnosis in industrial control systems.