2015;  to re-interpret the input noise as the intrinsic stochasticity in the embedding weights B itself as follows: where , where the random weights in each row has a tied variance/mean ratio α i .
we rewrite the evidence lower bound as follows: where P(B) is the prior distribution and N (µ, σ) denotes the Gaussian approximate posterior with parameters µ and σ. L(B, θ) is used as the relaxed evidence lower bound of marginal log likelihood log L(f θ (x), y)).