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arXiv Machine Learning By Junhao Zhao, David Michael Simberg, Jacob Kang, Colin Connor Kurniawan, Nan Xu

Large Distant Gradients Need Not Be Reliable: reliability-weighted credit assignment for long-horizon autoregressive forecasting

Read the original on arXiv Machine Learning →

The paper introduces Internal Dual-Wiener routing (Internal‑DW), a backward‑only method that weight‑balances internal gradient routes in autoregressive forecasting. By estimating bounded Wiener gains for identity and nonlinear paths, it suppresses unpredictable noise while preserving predictable learning signals, reducing forecast error by 5.2%–13.8% on four weak‑drive testbeds compared to full BPTT and outperforming gradient clipping, Jacobian regularization, and truncated BPTT in most cases. The approach shows that long‑horizon supervision can be effective without trusting every backward gradient equally.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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