Topic
Uncertainty and calibration research, explained
4 open-access uncertainty and calibration studies, each with a flashcard deck and a quiz.
- Do people learn like Bayesians when rewards suddenly change?
People chose like Bayesian learners who dislike uncertainty, but only when told that reward odds could suddenly jump; otherwise simple trial-and-error learning fit just as well.
- How should a learner speed up learning when the world keeps changing?
A simple extension of the Kalman filter that also tracks how fast the world is changing approximates optimal learning more accurately than the popular HGF and explains most people's choices better.
- Can a small neural network stand in for a supercomputer heart model?
A compact neural ODE learned to reproduce a detailed whole-heart simulator's pressure and volume curves with a few percent error, making heavy analyses feasible on a laptop.
- Which uncertainty methods help ML guide protein engineering?
No uncertainty-estimation method was best across protein tasks, and using uncertainty to choose which proteins to test next never beat simply picking the highest predicted ones.