Analogous to LDA-style topic models, it is assumed that there are K topics underlying the given corpus C. Each topic k is represented with a topic-word distribution φ k over the vocabulary, and each post x ∈ C has a topic mixture denoted by θ, a K-dim distributional vector.
in neural topic model, θ is constructed by Gaussian softmax (Miao et al., 2017).