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Algebraic Multigrid Acceleration for Efficient Label Spreading
Not provided
semi-supervised learninglabel spreadingmultigrid solvers
2608.26309
Builder Relevance
1h ago80%
Abstract
The paper proposes an efficient label spreading framework that improves scalability and reduces computational costs for large-scale datasets.
Reality Card
Core Claim
AMELS significantly reduces runtime and improves robustness to hyperparameter choices for label spreading on large-scale datasets.
Method / Result
Achieves significant runtime reductions compared to existing implementations.
Limitations
The paper does not provide specific details on the reproducibility of the results or the datasets used.
Paper to code
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