The KL Divergence term in Equation 2 can thus be analytically computed.
the KL Divergence between two K dimensional Gaussian distributions A, B with means µ A , µ B and diagonal co-variances κ A , κ B is: Parameter Estimation Having described precisely, the models for each of the components in Equation 2, we can reformulate the maximization of the variational lower bound to the following loss function on the set of all learn-able model parameters θ: J (θ) as follows: where NLL is the negative log likelihood loss computed between the predicted label and the true label, and λ is a hyper-parameter that controls the amount of regularization offered by the KL Divergence term.