@inproceedings{Theis2021c,
  author = "L. Theis and J. Ho",
  title = "Importance weighted compression",
  year = 2021,
  booktitle = "Neural Compression Workshop at ICLR",
  keywords = "compression, deep learning, bits back",
  url = "https://openreview.net/forum?id=n6skss_9-v3"
}


@inproceedings{Agustsson2020a,
  author = "E. Agustsson and L. Theis",
  title = "Universally Quantized Neural Compression",
  year = 2020,
  booktitle = "Advances in Neural Information Processing Systems 33",
  keywords = "compression, deep learning, channel simulation",
  url = "https://arxiv.org/abs/2006.09952"
}


@inproceedings{Nguyen-Phuoc2019a,
  author = "T. Nguyen-Phuoc and C. Li and L. Theis and C. Richardt and Y.-L. Yang",
  title = "HoloGAN: Unsupervised learning of 3D representations from natural images",
  year = 2019,
  booktitle = "International Conference on Computer Vision",
  keywords = "generative modeling, 3d, deep learning",
  url = "https://www.monkeyoverflow.com/#/hologan-unsupervised-learning-of-3d-representations-from-natural-images/"
}


@inproceedings{Korshunova2016a,
  author = "I. Korshunova and W. Shi and J. Dambre and L. Theis",
  title = "Fast Face-swap Using Convolutional Neural Networks",
  year = 2017,
  booktitle = "International Conference on Computer Vision",
  month = "Oct",
  keywords = "face-swap, cagenet, deep learning",
  url = "https://arxiv.org/abs/1611.09577"
}


@inproceedings{Ledig2016a,
  author = "C. Ledig and L. Theis and F. Huszar and J. Caballero and A. Aitken and A. Tejani and J. Totz and Z. Wang and W. Shi",
  title = "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network",
  year = 2017,
  booktitle = "Computer Vision and Pattern Recognition",
  month = "Jul",
  keywords = "super-resolution, deep learning",
  url = "http://arxiv.org/abs/1609.04802"
}


@inproceedings{Theis2017a,
  author = "L. Theis and W. Shi and A. Cunningham and F. Huszár",
  title = "Lossy Image Compression with Compressive Autoencoders",
  year = 2017,
  booktitle = "International Conference on Learning Representations",
  keywords = "compression, deep learning",
  url = "https://openreview.net/pdf?id=rJiNwv9gg"
}


@inproceedings{Sønderby2016a,
  author = "C. Sønderby and J. Caballero and L. Theis and W. Shi and F. Huszár",
  title = "Amortised MAP Inference for Image Super-resolution",
  year = 2017,
  booktitle = "International Conference on Learning Representations",
  keywords = "super-resolution, deep learning",
  url = "https://arxiv.org/abs/1610.04490"
}


@inproceedings{Theis2015c,
  author = "L. Theis and M. Bethge",
  title = "Generative Image Modeling Using Spatial LSTMs",
  year = 2015,
  booktitle = "Advances in Neural Information Processing Systems 28",
  month = "Dec",
  keywords = "deep learning, generative modeling, natural image statistics, lstm, mcgsm",
  url = "http://arxiv.org/abs/1506.03478/"
}


@inproceedings{Kümmerer2014b,
  author = "M. Kümmerer and L. Theis and M. Bethge",
  title = "Deep Gaze I: Boosting Saliency Prediction with Feature Maps Trained on ImageNet",
  year = 2015,
  booktitle = "ICLR Workshop",
  month = "Jun",
  keywords = "saliency, deep learning",
  url = "http://arxiv.org/abs/1411.1045"
}


@article{Theis2011a,
  author = "L. Theis and S. Gerwinn and F. Sinz and M. Bethge",
  title = "In All Likelihood, Deep Belief Is Not Enough",
  year = 2011,
  journal = "Journal of Machine Learning Research",
  volume = 12,
  month = "Nov",
  keywords = "natural image statistics, deep belief networks, boltzmann machines, deep learning"
}