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- W2519697148 abstract "This paper proposes a new inference for the correlated topic model (CTM) [3]. CTM is an extension of LDA [4] for modeling correlations among latent topics. The proposed inference is an instance of the stochastic gradient variational Bayes (SGVB) [7, 8]. By constructing the inference network with the diagonal logistic normal distribution, we achieve a simple inference. Especially, there is no need to invert the covariance matrix explicitly. We performed a comparison with LDA in terms of predictive perplexity. The two inferences for LDA are considered: the collapsed Gibbs sampling (CGS) [5] and the collapsed variational Bayes with a zero-order Taylor expansion approximation (CVB0) [1]. While CVB0 for LDA gave the best result, the proposed inference achieved the perplexities comparable with those of CGS for LDA." @default.
- W2519697148 created "2016-09-23" @default.
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- W2519697148 date "2016-01-01" @default.
- W2519697148 modified "2023-09-27" @default.
- W2519697148 title "A Simple Stochastic Gradient Variational Bayes for the Correlated Topic Model" @default.
- W2519697148 cites W2001082470 @default.
- W2519697148 doi "https://doi.org/10.1007/978-3-319-45817-5_39" @default.
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