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

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

Calibration: Model's predicted probabilities match actual outcomes' frequencies

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

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

Why ALiBi allows length extrapolation better than learned position embeddings

ALiBi uses fixed-length position encodings, enabling efficient length extrapolation without model retraining

What IS (Inception Score) measures: diversity and quality of generated images

Inception Score quantifies image diversity and generated images' quality

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