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Three novel creative-reasoning tasks requiring concrete solutions in low-constraint settings are introduced, suggesting distinct performance patterns across task structures: T-UoT is strongest in low-constraint, high-specificity settings, E-UoT in more constrained, high-specificity settings, and C-UoT in low-constraint...
Many machine learning (ML) applications rely on expert labels, and qualified experts may provide different but plausible interpretations of the same observation. Such variation across expert labels may reflect genuine disagreement or ambiguity rather than annotation error. When individual experts additionally report in...
This paper proposes a two-module framework where first Multiple Instance Learning is used for water--ice classification, and then a multi-label proportion learning (MLPL) is introduced for ice-type composition prediction, and extends this framework with a multimodal model that integrates SAR imagery with AMSR2 brightne...
TCA-SIR is presented, which learns to generate target-conditioned abstractions and uses their representations to predict transferability and outperforms prior SIR methods and direct LLM retrieval, improving HitRate@top4% over MOOSE-Chem by more than 10 percentage points.
Arctic maritime navigation is becoming increasingly important as changing sea-ice conditions expand seasonal accessibility while simultaneously introducing substantial operational, environmental, and community risks. Arctic route planning is inherently a multi-criteria problem: routes that improve vessel safety or effi...
Manifold learning is recast as multi-task learning: MT-LLE jointly optimizes five objectives: geometric reconstruction, supervised organization by clinical stage, embedding and phenotype forecasting, and clustering, which supports complementary analyses that surface structure invisible to standard staging.
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