Conformal Seasonal Pools is a training-free method that outperforms DeepNPTS on CRPS, quantile loss, and especially 95% coverage (0.89 vs 0.66) across six time-series datasets while being over 500x faster on CPU.
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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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2026 3representative citing papers
Hard-label delivery via multipass or SLS matches or beats soft-label training on annotator disagreement data when annotations are sparse and leads to flatter minima.
CredibleDFGO extends DFGO by training a weighting network with NLL and energy score supervision so that the Hessian-derived covariances better match actual positioning errors on UrbanNav scenes.
citing papers explorer
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CredibleDFGO: Differentiable Factor Graph Optimization with Credibility Supervision
CredibleDFGO extends DFGO by training a weighting network with NLL and energy score supervision so that the Hessian-derived covariances better match actual positioning errors on UrbanNav scenes.