Constructing Dynamic Topic Models Based on Variational Autoencoder and Factor Graph

Constructing Dynamic Topic Models Based on Variational Autoencoder and Factor Graph

Topic models are widely used in various fields of machine learning and statistics. Among them, the dynamic topic model (DTM) is the most popular time-series topic model for the dynamic representations of text corpora. A major challenge is that the posterior distribution of DTM requires a complex rea...

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Bibliographic Details
Main Authors: Zhinan Gou, Lixin Han, Ling Sun, Jun Zhu, Hong Yan
Format: Article
Language:English
Published: IEEE 2018-01-01
Series:IEEE Access
Subjects:
Dynamic topic model
variational autoencoder
factor graph
2-layer neural network
Online Access:https://ieeexplore.ieee.org/document/8464062/

Internet

https://ieeexplore.ieee.org/document/8464062/

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