NEPF decomposes routing policies into node permutation and edge selection stages for scalable solving of multigraph VRPs, achieving competitive quality with faster training and inference.
Kingma and Jimmy Ba , title =
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
SelectiveRM applies optimal transport with a joint consistency discrepancy and partial mass relaxation to produce reward models that optimize a tighter upper bound on clean risk while autonomously dropping noisy preference samples.
A deep learning model generates image-aware poster layouts that satisfy user-specified attribute constraints via Gaussian noise sampling and partial layout constraints via a dedicated loss and random mask, reaching state-of-the-art performance.
citing papers explorer
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Two-Stage Learned Decomposition for Scalable Routing on Multigraphs
NEPF decomposes routing policies into node permutation and edge selection stages for scalable solving of multigraph VRPs, achieving competitive quality with faster training and inference.
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Optimal Transport for LLM Reward Modeling from Noisy Preference
SelectiveRM applies optimal transport with a joint consistency discrepancy and partial mass relaxation to produce reward models that optimize a tighter upper bound on clean risk while autonomously dropping noisy preference samples.
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Image-aware Layout Generation with User Constraints for Poster Design
A deep learning model generates image-aware poster layouts that satisfy user-specified attribute constraints via Gaussian noise sampling and partial layout constraints via a dedicated loss and random mask, reaching state-of-the-art performance.