Which of the following initialization schemes will ensure that gradients and activations do not blow up or vanish in a deep network?

Group of answer choices

Xavier initialization $($scale variance by d$\_$in and d$\_$out$)$

Zero initialization

Random initialization with constant variance across layers

Constant initialization of W$=10$