SetFlow is a flow-matching generative model for permutation-invariant MIL bags in representation space that produces synthetic data improving classification performance and enabling training on synthetic data alone.
Set trans- former: A framework for attention-based permutation-invariant neural networks
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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Introduces the relation set completion task and RelSetE embedding model to infer missing entity-compatible relations by modeling latent patterns in observed relations, evaluated on three derived KG benchmarks.
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SetFlow: Generating Structured Sets of Representations for Multiple Instance Learning
SetFlow is a flow-matching generative model for permutation-invariant MIL bags in representation space that produces synthetic data improving classification performance and enabling training on synthetic data alone.
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Beyond Triplet Plausibility: Relation Set Completion in Knowledge Graphs
Introduces the relation set completion task and RelSetE embedding model to infer missing entity-compatible relations by modeling latent patterns in observed relations, evaluated on three derived KG benchmarks.