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English(EN) Inference-Time Decision Calibration for Temporal Classification

新方法通过分离表示和决策误差来校准时序分类

作者 PulseAugur 编辑部 · [1 个来源] ·

本文介绍了一种新颖的时序分类方法，通过将误差分解为表示失败和决策问题。所提出的方法包括冻结已训练的分类器，并添加两个推理时干预：一个用于辅助逻辑的多尺度残差分支和一个用于重新组合证据的分支感知校准器。在FI-2010和PTB-XL等各种数据集上的实验表明，这些干预措施带来了显著的收益，尤其是在噪声或表示受限的情况下，这表明时序分类受益于改进的证据校准以及表示学习。 AI

排序理由 该集群包含一篇关于时序分类新方法的学术论文，已提交至arXiv。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新方法通过分离表示和决策误差来校准时序分类

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该集群包含一篇关于时序分类新方法的学术论文，已提交至arXiv。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Arthur Chagas, Arthur Buzelin, Yan Aquino, Pedro Bento, Gisele L. Pappa, Wagner Meira Jr., Cristiano Arbex Valle ·

    面向时序分类的推理时决策校准

    arXiv:2606.16034v1 Announce Type: new Abstract: Temporal classification errors are often treated as representation failures, but they can also arise from how available evidence is converted into decisions. This paper proposes a representation--calibration decomposition for tempor…

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