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.
Emerging properties in self-supervised vision transformers
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Decouples semantic and spatial tokens in NVS transformers to resolve representation ambiguity, yielding consistent gains with near-zero added latency.
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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Resolving Representation Ambiguity in Feedforward Novel View Synthesis Transformer via Semantic-Spatial Decoupling
Decouples semantic and spatial tokens in NVS transformers to resolve representation ambiguity, yielding consistent gains with near-zero added latency.