New method calibrates temporal classification by separating representation and decision errors

This paper introduces a novel approach to temporal classification by decomposing errors into representation failures and decision-making issues. The proposed method involves freezing a trained classifier and adding two inference-time interventions: a multi-scale residual branch for auxiliary logits and a branch-aware calibrator to recombine evidence. Experiments on various datasets like FI-2010 and PTB-XL show that these interventions yield significant gains, particularly in noisy or representation-limited scenarios, suggesting that temporal classification benefits from improved evidence calibration alongside representation learning. AI

RANK_REASON The cluster contains an academic paper detailing a new method for temporal classification, submitted to arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• FI-2010
• HARTH
• Hugging Face
• PTB-XL
• UCI-HAR

• paper
• other

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