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VAE Learning via Stein Variational Gradient Descent
Yuchen Pu · Zhe Gan · Ricardo Henao · Chunyuan Li · Shaobo Han · Lawrence Carin

Tue Dec 05 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #121

A new method for learning variational autoencoders (VAEs) is developed, based on Stein variational gradient descent. A key advantage of this approach is that one need not make parametric assumptions about the form of the encoder distribution. Performance is further enhanced by integrating the proposed encoder with importance sampling. Excellent performance is demonstrated across multiple unsupervised and semi-supervised problems, including semi-supervised analysis of the ImageNet data, demonstrating the scalability of the model to large datasets.

Author Information

Yuchen Pu (Duke University)
Zhe Gan (Duke University)
Ricardo Henao (Duke University)
Chunyuan Li (Duke University)

Chunyuan is a PhD student at Duke University, affiliated with department of Electrical and Computer Engineering, advised by Prof. Lawrence Carin. His recent research interests focus on scalable Bayesian methods for deep learning, including generative models and reinforcement learning, with applications to computer vision and natural language processing.

Shaobo Han (Duke University)
Lawrence Carin (Duke University)

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