A contrastive learning method learns stable representations from rainfall and terrain data to improve short-term landslide risk estimates under rainfall forecast displacement, reporting up to 37% higher precision than baselines on Japanese data.
Supervised contrastive learn- ing,
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RedParrot accelerates NL-to-DSL conversion by 3.6x with 8.26% accuracy gain on enterprise data and 34.8% on benchmarks via semantic caching of query skeletons and contrastive learning.
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Learning Displacement-Robust Representations for Landslide Early Warning under Rainfall Forecast Uncertainty
A contrastive learning method learns stable representations from rainfall and terrain data to improve short-term landslide risk estimates under rainfall forecast displacement, reporting up to 37% higher precision than baselines on Japanese data.
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RedParrot: Accelerating NL-to-DSL for Business Analytics via Query Semantic Caching
RedParrot accelerates NL-to-DSL conversion by 3.6x with 8.26% accuracy gain on enterprise data and 34.8% on benchmarks via semantic caching of query skeletons and contrastive learning.