What calibration means: a model predicting 80% should be correct 80% of the time

What expected calibration error (ECE) measures: gap between confidence and accuracy

ECE quantifies the discrepancy between a model's predicted confidence and its actual accuracy

What label smoothing does: replaces one-hot [0,0,1,0] with [0.025, 0.025, 0.925, 0.025]

Label smoothing regularizes models by adjusting target distributions

A p-value < 0.05 means: if H₀ is true, this result has <5% probability

A p-value < 0.05 indicates a less than 5% chance of observing data as extreme as this if the null hypothesis is true

How does the Root Mean Square Error (RMSE) quantify the difference between predicted values and observed values in regression analysis?

RMSE measures the average magnitude of prediction errors in regression, squaring and averaging residuals

What score matching does: learns the gradient of the log-density without normalizing

Score matching approximates log-density gradients for variational inference without normalization

How does score matching utilize the Fisher Information Matrix to learn the parameters of a probabilistic model without normalizing the score?

Score matching estimates parameters by minimizing the Kullback-Leibler divergence between empirical and model score distributions