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Latent Diffusion Energy-Based Model for Interpretable Text Modelling
Peiyu Yu · Sirui Xie · Xiaojian Ma · Baoxiong Jia · Bo Pang · Ruiqi Gao · Yixin Zhu · Song-Chun Zhu · Ying Nian Wu

Tue Jul 19 03:30 PM -- 05:30 PM (PDT) @ Hall E #116

Latent space Energy-Based Models (EBMs), also known as energy-based priors, have drawn growing interests in generative modeling. Fueled by its flexibility in the formulation and strong modeling power of the latent space, recent works built upon it have made interesting attempts aiming at the interpretability of text modeling. However, latent space EBMs also inherit some flaws from EBMs in data space; the degenerate MCMC sampling quality in practice can lead to poor generation quality and instability in training, especially on data with complex latent structures. Inspired by the recent efforts that leverage diffusion recovery likelihood learning as a cure for the sampling issue, we introduce a novel symbiosis between the diffusion models and latent space EBMs in a variational learning framework, coined as the latent diffusion energy-based model. We develop a geometric clustering-based regularization jointly with the information bottleneck to further improve the quality of the learned latent space. Experiments on several challenging tasks demonstrate the superior performance of our model on interpretable text modeling over strong counterparts.

Author Information

Peiyu Yu (University of California, Los Angeles)
Sirui Xie (UCLA)
Xiaojian Ma (Tsinghua University)

Xiaojian Ma is a senior undergraduate student at Tsinghua University and will be an incoming graduate student at the University of California, Los Angeles working with Prof. Song-chun Zhu. Previously he has been a visiting student at the National University of Singapore, with Prof. Gim Hee Lee. He also spends time at Tencent AI Lab, mentored by Dr. Wenbing Huang. His research interest including machine learning in general (statistical learning, learning theory, and reinforcement learning) and its application in robotics (imitation learning, optimal control, manipulation, grasping and multimodel sensing). Some of his research has been accepted as full papers or workshop papers to top-tier machine learning and robotics conferences as AAAI/ICRA/IROS/CVPR.

Baoxiong Jia (UCLA)
Bo Pang (University of California Los Angeles)
Ruiqi Gao (UCLA)
Yixin Zhu (Peking University)
Song-Chun Zhu (UCLA)
Ying Nian Wu (UCLA)

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