English(EN) Seq-Flow: Efficient Probabilistic Forecasting with Self-Rollout Error Control

Seq-Flow 模型提供具有误差控制的高效概率预测

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-08 04:00

研究人员开发了 Seq-Flow，一种新颖的条件流模型，用于高效概率预测。该模型利用 ODE 将样本从先前的预测分布传输到更新的分布，显著减少了所需的采样步数。为了减轻顺序更新带来的误差累积，Seq-Flow 采用了一种自回滚训练方法，其中模型的移动平均副本会初始化后续训练。实验证明了 Seq-Flow 在粒子加速器束流泄漏预测方面的有效性，在有限的采样预算下将 CRPS 降低了 65%，并在数百次更新中保持了准确性。 AI

影响为科学任务中的概率预测引入了一种更有效的方法，有可能提高准确性并降低计算成本。

排序理由该集群包含一篇详细介绍新模型及其实验结果的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Yinan Huang, Shitij Govil, Bo Dai, Pan Li · 2026-10-08 04:00

Seq-Flow：具有自回滚误差控制的高效概率预测

arXiv:2610.10440v1 Announce Type: new Abstract: Many scientific forecasting tasks require updating a distribution over future trajectories as new observations arrive. Conventional diffusion and flow models generate each forecast from Gaussian noise, often at the cost of many samp…

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Seq-Flow：具有自回滚误差控制的高效概率预测

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