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English(EN) State-Space Unlearning for Non-Stationary Bias in Land Surface Forecasting

新的机器学习遗忘方法解决了时间序列预测中的偏差和数据删除问题

作者 PulseAugur 编辑部 · [2 个来源] ·

两篇新研究论文介绍了针对时间序列预测的新型机器学习遗忘技术。第一篇论文 SSU-LSF 通过开发一个框架来消除非平稳混杂事件的影响，解决了 Mamba 等陆面预测模型中的偏差问题。第二篇论文 RDTU 提出了一个残差扩散框架来处理时间序列预测中的数据删除请求，解决了参数更新传播和已删除模式的非均匀支持等挑战。 AI

影响 这些新的遗忘技术可以改善敏感时间序列预测应用中的数据隐私和治理。

排序理由 arXiv 上发表的两篇学术论文，介绍了用于时间序列预测的新机器学习遗忘技术。

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

新的机器学习遗忘方法解决了时间序列预测中的偏差和数据删除问题

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arXiv 上发表的两篇学术论文，介绍了用于时间序列预测的新机器学习遗忘技术。
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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Anidipta Pal ·

    用于非平稳偏差的陆地表面预测的状态空间遗忘

    arXiv:2610.02248v1 Announce Type: new Abstract: Operational land surface forecasting systems built on Mamba-family Structured State Space Models absorb non-stationary confounding events (unrecorded irrigation booms, dam-operation shifts, sensor recalibrations) into their state-tr…

  2. arXiv cs.LG TIER_1 English(EN) · Zeyu Shi, Yanhui Luo, Ziming Hong, Chongyang Gao, Kezhen Chen, Shanshan Ye, Lixu Wang ·

    关于时间序列预测的非学习

    arXiv:2610.02865v1 Announce Type: new Abstract: Time-series forecasting is widely used in sensitive domains. Models in these settings are often trained on longitudinal user- or entity-level records, which may later require removal because they contain sensitive or proprietary inf…

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