DynLP is a parallel dynamic batch update algorithm for label propagation that achieves significant speedups by updating only relevant parts of the graph on GPUs.
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3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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iCoRe improves Fail-to-Pass rates to 42.0% and 52.8% on two bug reproduction benchmarks by using correlation-aware iterative retrieval instead of standard semantic or BM25 methods.
AGREE boosts visual document retrieval by adding local relevance signals from MLLM attention maps to global document labels during retriever training.
citing papers explorer
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DynLP: Parallel Dynamic Batch Update for Label Propagation in Semi-Supervised Learning
DynLP is a parallel dynamic batch update algorithm for label propagation that achieves significant speedups by updating only relevant parts of the graph on GPUs.
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iCoRe: An Iterative Correlation-Aware Retriever for Bug Reproduction Test Generation
iCoRe improves Fail-to-Pass rates to 42.0% and 52.8% on two bug reproduction benchmarks by using correlation-aware iterative retrieval instead of standard semantic or BM25 methods.
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Attention Grounded Enhancement for Visual Document Retrieval
AGREE boosts visual document retrieval by adding local relevance signals from MLLM attention maps to global document labels during retriever training.