This might tell us, for instance, how typically we could expect failure of a method that depended on these assumptions.

InAdvancesin Neural Information Processing Systems, 2020, to appear.

Although highly desirable in many applications and in spite of its omnipresence in machine learning, current neural network architectures do not equivary to normalization.

Traditionally, it has relied on human intuition, leading to the discovery of some of the most famous formulas in science.

Notethatthere(2) (see[4]) F is Therefore ( ) asablack-box distributionˆF.