  We propose a number of new algorithms for learning deep energy models and
demonstrate their properties. We show that our SteinCD performs well in term of
test likelihood, while SteinGAN performs well in terms of generating realistic
looking images. Our results suggest promising directions for learning better
models by combining GAN-style methods with traditional energy-based learning.
