Tweedie's formula, we show that we can perform gradient steps to sequentially optimize these objectives.

Most applications of machine learning to classification assume a closed set of balanced classes.

However, existing frameworks face several interrelated limitations.

This paper presents the first benchmark of uncertainty disentanglement.

It appears that individuals experience different numbers of small and large growth spurts that differ in magnitude and timing.