I’ve actually trained a neural net with 1k parameters and another guy on github trained a NN with 9k parameters for our benchmark (GitHub - open-spaced-repetition/srs-benchmark: A benchmark for spaced repetition schedulers/algorithms, LSTM), and it outperforms FSRS, so overfitting isn’t that big of an issue. Plus, regularizaiton exists for a reason.
He also made a neural net with 2.7 million parameters, but that one is different - it’s not optimized on the data of every individual user, it’s “pretrained” on 5 thousand users and evaluated on the other 5, and that is repeated twice to cover the entire dataset. I’m actually surprised that it works so well. This is a different approach compared to all other algorithms that we have benchmarked. Other algorithms are trained on each user individually.
Bigger issues are:
- More parameters = slower optimization.
- FSRS isn’t a neural net. I can’t add a thousand parameters by changing one line of code. And the philosophy of FSRS is keeping it interpretable, so even if I made a hybrid neural FSRS, Jarrett wouldn’t approve. Well, I wouldn’t either, unless the benefits were absolutely massive.