arXiv：cs.LG· Anders Schill·· 4 小时前
SBD：面向间隔重复的可解释记忆模型，比 SOTA 小 80%
Interpretable Memory Models for Spaced Repetition
AI 导读
研究提出记忆模型 SBD，用于间隔重复软件的复习调度，在准确率几乎持平当前 SOTA 的前提下体积小 80%，且更易做机制性解释。作者指出，仅靠测试集准确率不足以证明模型质量，因为训练数据由现有调度器产生，新方案必须具备外推能力。
正文
Abstract:Spaced repetition software schedules reviews with a memory model fit to review logs. Accuracy on a test set is not sufficient evidence of quality since available data are produced by existing schedulers, and new solutions must extrapolate beyond them. A model also needs a simple mechanistic interpretation. We present SBD, a model that is more interpretable and 80% smaller than the current state of the art at nearly the same accuracy.
| Comments: | Also available on Zenodo: doi:https://doi.org/10.5281/zenodo.22727106 |
| Subjects: | Neurons and Cognition (q-bio.NC); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.10548 [q-bio.NC] |
| (or arXiv:2610.10548v1 [q-bio.NC] for this version) | |
| https://doi.org/10.48550/arXiv.2610.10548 arXiv-issued DOI via DataCite |
Submission history
From: Anders Schill [view email]
[v1]
Mon, 14 Sep 2026 19:48:20 UTC (21 KB)
来源：arXiv：cs.LG · arxiv.org