Interpretable Memory Models for Spaced Repetition

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.

Also available on Zenodo: doi:https://doi.org/10.5281/zenodo.22727106

Neurons and Cognition (q-bio.NC); Machine Learning (cs.LG)

arXiv:2610.10548 [q-bio.NC]

(or arXiv:2610.10548v1 [q-bio.NC] for this version)

https://doi.org/10.48550/arXiv.2610.10548 Focus to learn more arXiv-issued DOI via DataCite

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