HGPM learns compositional patterns in hypergraphs by subset tokenization and inclusion-aware masked Transformer reconstruction, matching or exceeding SOTA on ten benchmarks and correctly identifying inhibitory drug additions in adverse-event prediction where prior methods fail.
Adaptive expansion for hypergraph learning
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Brick-DICL applies dynamic in-context learning with two RAG stages and multi-LLM filtering to automate mapping of BMS points to the 936-class Brick ontology, claiming accuracy gains and reduced manual verification.
citing papers explorer
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Hypergraph Pattern Machine: Compositional Tokenization for Higher-Order Interactions
HGPM learns compositional patterns in hypergraphs by subset tokenization and inclusion-aware masked Transformer reconstruction, matching or exceeding SOTA on ten benchmarks and correctly identifying inhibitory drug additions in adverse-event prediction where prior methods fail.
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Brick-DICL: Dynamic In-Context Learning for Automated Brick Schema Classification
Brick-DICL applies dynamic in-context learning with two RAG stages and multi-LLM filtering to automate mapping of BMS points to the 936-class Brick ontology, claiming accuracy gains and reduced manual verification.