Deterministic Inference of Topic Models via Maximal Latent State Replication

Probabilistic topic models, such as latent dirichlet allocation (LDA), are often used to discover hidden semantic structure of a collection of documents. In recent years, various inference algorithms have been developed to cope with learning of topic models, among which Gibbs sampling methods remain a popular choice. In this paper, we aim to improve the inference of topic models based on the Gibbs...