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Machine Learning Foundations
How models learn from data, and how we know they have.
9 studies across 4 topics
Uncertainty and calibration
4 studies
Uncertainty in learning and prediction
4
Do people learn like Bayesians when rewards suddenly change?
How should a learner speed up learning when the world keeps changing?
Can a small neural network stand in for a supercomputer heart model?
Which uncertainty methods help ML guide protein engineering?
Generalisation and overfitting
3 studies
Generalization and sample size
4
Can one neural network score sleep across any clinic's data?
How much data do you need before ML predictions can be trusted?
Does a surgery-death prediction model work at other hospitals?
Supervised learning
1 studies
Can spiking neural networks learn precise timing with hidden layers?
Model evaluation
1 studies
Can effect size tell you if your ML data set is big enough?