Variational Inference In Pachinko Allocation Machines

Akash Srivastava; Charles Sutton

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arXiv · 1804.07944

Variational Inference In Pachinko Allocation Machines

Akash Srivastava·Charles Sutton

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Abstract

The Pachinko Allocation Machine (PAM) is a deep topic model that allows representing rich correlation structures among topics by a directed acyclic graph over topics. Because of the flexibility of the model, however, approximate inference is very difficult. Perhaps for this reason, only a small number of potential PAM architectures have been explored in the literature. In this paper we present an efficient and flexible amortized variational inference method for PAM, using a deep inference network to parameterize the approximate posterior distribution in a manner similar to the variational autoencoder. Our inference method produces more coherent topics than state-of-art inference methods for PAM while being an order of magnitude faster, which allows exploration of a wider range of PAM architectures than have previously been studied.

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Akash Srivastava, Charles Sutton. 2018-04-21. Variational Inference In Pachinko Allocation Machines. https://arxiv.org/abs/1804.07944

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