Taking the logarithm of both sides, we obtain where m ik = L i j=1 δ(z ij = k) counts the number of words assigned with the k-th topic in 6 Variational Inference Algorithm Given the hyperparameters α, γ, µ, the learning objective is to find the embeddings V , the topics T , and the word-topic and document-topic distributions Here the hyperparameters α, γ, µ are kept constant, and we make them implicit in the distribution notations.
hinton and Salakhutdinov (2009) proposed to model topics as a certain number of binary hidden variables, which interact with all words in the doc-ument through weighted connections.