The goal here is not to build an ensemble of DNNs to surpass current classification state of the art results, but instead to test our hypothesis to determine if machine RPs can fit IRT models that can benefit NLP tasks.
as this posterior is usually intractable, VI approximates it by the variational distribution: Where π θ j () and π b i () denotes different Gaussian densities for different parameters whose means and variances are determined by minimizing the KL-Divergence between q(θ, b) and π(θ, b|Y ).