Comments (3)
There are two ways for auto-optimization: Online Learning and Periodic Retraining. I prefer the latter because it is easier to implement and more stable.
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To implement it, we need to integrate the scheduler into Anki at first. By the way, online learning doesn't mean that it must have experience replay. The main feature of online learning is it could optimize its current weights by the new reviews on the fly.
from fsrs-rs.
To implement it, we need to integrate the scheduler into Anki at first.
Got it.
By the way, online learning doesn't mean that it must have experience replay.
I know that. My logic was that if you had implemented online learning, you would also have implemented experience replay to make the results better.
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Related Issues (20)
- [Enhancement] Use more splits while training with larger datasets HOT 3
- Request: Ignore reviews before "Forget" HOT 9
- Enhancement: Include incomplete revlogs even when training HOT 4
- Consider time-frame limitation? HOT 3
- TODO: speed up finding optimal retention via Brent's method
- Better outlier filter for trainset HOT 25
- Skip reviews with time = 0 when calculating average answer times HOT 1
- What's the difference between this repo and rs-fsrs? HOT 1
- User guide HOT 3
- Add an option to turn off outlier filter when benchmark HOT 1
- Inference.rs uses the new power curve, but the default parameters are from v4 HOT 17
- Add a example file HOT 1
- Reference usage? HOT 5
- Pre-training Only when the number of reviews is less than 1000 HOT 5
- [BUG] Potential inconsistency in optimal_retention.rs HOT 20
- [Question] How to choose "Days to simulate"? HOT 14
- [Feature Request[ Use two different sets of initial parameters, then average out the results HOT 4
- Use the first revlog in the "known" review history for converting SM-2 ivl & ease to memory states HOT 13
- Achieve parity with the Python optimizer HOT 10
- support WASM HOT 4
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