@MASTERSTHESIS\{IMM2014-06742, author = "L. Maal{\o}e", title = "Deep Belief Nets Topic Modeling", year = "2014", school = "Technical University of Denmark, Department of Applied Mathematics and Computer Science", address = "Matematiktorvet, Building 303B, {DK-}2800 Kgs. Lyngby, Denmark, compute@compute.dtu.dk", type = "", note = "{DTU} supervisor: Ole Winther, olwi@dtu.dk, {DTU} Compute, external supervisor Morten Arngren from Issuu", url = "http://www.compute.dtu.dk/English.aspx", abstract = "This thesis is conducted in collaboration with Issuu, an online publishing company. In order to analyze the vast amount of documents on the platform, Issuu use Latent Dirichlet Allocation as a topic model. Geoffrey Hinton \& Ruslan Salakhutdinov have introduced a new way to perform topic modeling, which they claim can outperform Latent Dirichlet Allocation. The topic model is based on the theory of Deep Belief Nets and is a way of computing the conceptual meaning of documents into a latent representation. The latent representation consists of a reduced dimensionality of binary numbers, which proves to be useful when comparing documents. The thesis comprises the development of a toolbox for the Deep Belief Nets for topic modeling by which performance measurements has been conducted on the model itself and as a comparison to Latent Dirichlet Allocation." }