PLMA combines cross-graph attention EBMs with short warm-started MCMC chains to reach near-zero average optimality gaps on QAPLIB and strong robustness on hard Taixxeyy instances.
Learning combinatorial embedding networks for deep graph matching
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
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Semantic relations between objects and structural elements filter candidate graph matches in SLAM, cutting ambiguity and computation in symmetric indoor environments.
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Learning to Solve the Quadratic Assignment Problem with Warm-Started MCMC Finetuning
PLMA combines cross-graph attention EBMs with short warm-started MCMC chains to reach near-zero average optimality gaps on QAPLIB and strong robustness on hard Taixxeyy instances.
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Robust Graph Matching through Semantic Relationship Generation for SLAM
Semantic relations between objects and structural elements filter candidate graph matches in SLAM, cutting ambiguity and computation in symmetric indoor environments.