Introduces set-level structural priors induced from leaf-level supervision for semi-supervised hyperbolic hierarchical clustering to improve non-leaf structure consistency.
& Wright, J
2 Pith papers cite this work, alongside 63 external citations. Polarity classification is still indexing.
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Pith papers citing it
63
external citations · OpenAlex
years
2026 2verdicts
UNVERDICTED 2representative citing papers
The work demonstrates black-box certifiable randomness from single-particle quantum measurements without requiring a random seed.
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Semi-Supervised Hyperbolic Hierarchical Clustering with Set-Level Structural Priors
Introduces set-level structural priors induced from leaf-level supervision for semi-supervised hyperbolic hierarchical clustering to improve non-leaf structure consistency.
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Genuine certifiable randomness from a black-box
The work demonstrates black-box certifiable randomness from single-particle quantum measurements without requiring a random seed.