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arXiv Machine Learning By Hongyu Zhu, Lin Chen, Jing Chen, Yuting Zhou, Mingsheng Shang

Adaptive Forgetting for Nonstationary Optimization: Towards Robust EEG Decoding

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The paper introduces AFOR, a tensor‑wise adaptive optimizer for EEG decoding that replaces the fixed second‑moment decay coefficient used in Adam/AdamW with a dynamic coefficient estimated online from local gradient state. AFOR combines a Residual‑Alignment Signal Scorer (RASS) to assess gradient quality and an Adaptive Forgetting Controller (AFC) to map this score to a bounded per‑step decay coefficient, with cumulative‑product initialization correction for consistency. In cross‑subject experiments on three EEG benchmarks, AFOR outperforms standard optimizers, improving mean test accuracy by 3.00%, 2.07%, and 4.38% over Adam.

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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