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Spotlight
Bayesian Deep Embedding Topic Meta-Learner
Zhibin Duan · Yishi Xu · Jianqiao Sun · Bo Chen · Wenchao Chen · CHAOJIE WANG · Mingyuan Zhou

Wed Jul 20 11:35 AM -- 11:40 AM (PDT) @ Room 301 - 303

Existing deep topic models are effective in capturing the latent semantic structures in textual data but usually rely on a plethora of documents. This is less than satisfactory in practical applications when only a limited amount of data is available. In this paper, we propose a novel framework that efficiently solves the problem of topic modeling under the small data regime. Specifically, the framework involves two significant innovations: a bi-level generative model that aims to exploit the task information to guide the document generation, and a topic meta-learner that strives to learn a group of global topic embeddings so that fast adaptation to the task-specific topic embeddings can be achieved with a few gradient descent steps. We apply the proposed framework to a hierarchical embedded topic model and achieve better performance against various baseline models on diverse experiments, including few-shot topic discovery and few-shot document classification.

Author Information

Zhibin Duan (Xidian University)
Yishi Xu (Xidian University)
Jianqiao Sun (Xidian University)
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.

Wenchao Chen (Xi'dian University)
CHAOJIE WANG (XIDIAN UNIVERSITY)
Mingyuan Zhou (University of Texas at Austin)

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