title: Variational Lossy Autoencoder
abstract: Representation learning seeks to expose certain aspects of observed data in a learned representation that's amenable to downstream tasks like classification. 
  For instance, a good representation for 2D images might be one that describes only global structure and discards information about detailed texture. 
  In this paper, we present a simple but principled method to learn such global representations by combining Variational Autoencoder (VAE) with neural autoregressive models such as RNN, MADE and PixelRNN/CNN. 
  Our proposed VAE model allows us to have control over what the global latent code can learn and , by designing the architecture accordingly, we can force the global latent code to discard irrelevant information such as texture in 2D images, and hence the code only ``autoencodes'' data in a lossy fashion.
  In addition, by leveraging autoregressive models as both prior distribution $p(z)$ and decoding distribution $p(x|z)$, we can greatly improve generative modeling performance of VAEs, achieving new state-of-the-art results on MNIST, OMNIGLOT and Caltech-101 as well as competitive results on CIFAR10.  
  
authors: Xi Chen Diederik P. Kingma Tim Salimans Yan Duan Prafulla Dhariwal John Schulman Ilya Sutskever Pieter Abbeel
hIndex: Pieter Abbeel 126