  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.
