Convolutional Poisson Gamma Belief Network
CHAOJIE WANG · Bo Chen · SUCHENG XIAO · Mingyuan Zhou

Tue Jun 11th 05:05 -- 05:10 PM @ Room 101

To analyze a text corpus, one often resorts to a lossy representation that either completely ignores word order or embeds the words as low-dimensional dense feature vectors. In this paper, we propose convolutional Poisson factor analysis (CPFA) that directly operates on a lossless representation that processes the words in each document as a sequence of high-dimensional one-hot vectors. To boost its performance, we further propose the convolutional Poisson gamma belief network (CPGBN) that couples CPFA with the gamma belief network via a novel probabilistic pooling layer. CPFA forms words into phrases and captures very specific phrase-level topics, and CPGBN further builds a hierarchy of increasingly more general phrase-level topics. We develop both an upward-downward Gibbs sampler, which makes the computation feasible by exploiting the extreme sparsity of the one-hot vectors, and a Weibull distribution based convolutional variational auto-encoder that makes CPGBN become even more scalable in both training and testing. Experimental results demonstrate that CPGBN can extract high-quality text latent representations that capture the word order information, and hence can be leveraged as a building block to enrich a wide variety of existing discrete latent variable models that ignore word order.

Author Information

Bo Chen (School of Electronic Engineering, Xidian University)

Bo Chen, Ph.D., Professor. Before joining the Department of Electronic Engineering in Xidian University in 2013, I was a post-doc researcher, research scientist and senior research scientist at the Department of Electrical and Computer Engineering in Duke University. In 2013 and 2014, I was elected into the Program for New Century Excellent Talents in University and the Program for Thousand Youth Talents respectively. I am interested in developing statistical machine learning methods for the complex and large-scale data. My current interests are in statistical signal processing, statistical machine learning, deep learning and their applications to radar target detection and recognition.

Mingyuan Zhou (University of Texas at Austin)

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