Graph doubling reduces ultrabubble computation in bidirected graphs to weak superbubble detection, giving the first linear-time algorithm for the former.
14 The Power of Graph Doubling: Reducing Ultrabubbles to Weak Superbubbles 2 Ouahiba Bessouf, Abdelkader Khelladi, and Thomas Zaslavsky
3 Pith papers cite this work. Polarity classification is still indexing.
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The anti-lexicographic SUS-anchor achieves sampling densities less than 1% above the lower bound for alphabet size 4 and k=1, substantially outperforming bidirectional anchors.
LLMs exhibit Bayesian-like hypothesis updating with strong-sampling bias and an evaluation-generation gap but generalize poorly outside observed data.
citing papers explorer
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The Power of Graph Doubling: Computing Ultrabubbles in a Bidirected Graph by Reducing to Weak Superbubbles
Graph doubling reduces ultrabubble computation in bidirected graphs to weak superbubble detection, giving the first linear-time algorithm for the former.
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The anti-lexicographic SUS-anchor: a near-optimal k=1 sampling scheme
The anti-lexicographic SUS-anchor achieves sampling densities less than 1% above the lower bound for alphabet size 4 and k=1, substantially outperforming bidirectional anchors.
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Hypothesis generation and updating in large language models
LLMs exhibit Bayesian-like hypothesis updating with strong-sampling bias and an evaluation-generation gap but generalize poorly outside observed data.