weight initialization is important for modern deep learning. To understand [1,2], I would like to understand the following:
$$ E[x^2] = 0.5 Var[y], $$
where $x= max(0,y)$, $E[.]$ is the expectation, $Var[.]$ the variance, $x,y$ are random variables. We assume $y$ to have zero mean and to be symmetrical around the mean.
Thanx for an explanation/derivation