新方法通过可靠性加权梯度改进自回归预测

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-14 04:00

研究人员推出了一种名为内部双维纳路由（Internal-DW）的新方法，通过解决反向传播过程中（BPTT）远距离梯度不可靠的问题来改进自回归预测。该技术选择性地加权内部梯度路径，平衡了可预测学习信号的保留与不可预测噪声的抑制。在以历史为主的测试平台上进行的实验表明，与标准 BPTT 相比，Internal-DW 将预测误差降低了高达 13.8%，并且优于其他正则化方法。 AI

影响引入了一种增强长周期预测模型中梯度更新可靠性的技术。

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Junhao Zhao, David Michael Simberg, Jacob Kang, Colin Connor Kurniawan, Nan Xu · 2026-09-14 04:00

大型远距离梯度不一定可靠：用于长周期自回归预测的可靠性加权信用分配

arXiv:2609.12890v1 Announce Type: new Abstract: In autoregressive forecasting, long prediction rollouts provide distant supervision, but backpropagation through time (BPTT) carries gradients from those losses through many autoregressive steps. Repeated Jacobian products can make …

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大型远距离梯度不一定可靠：用于长周期自回归预测的可靠性加权信用分配

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