In these lectures, we discuss autoregressive generative models such as NADE, MADE, PixelCNN, PixelRNN, and the PixelVAE.

**Slides:**

- Autoregressive Generative Models (slides from Hugo Larochelle, Vincent Dumoulin and Aaron Courville)
- FiLM (question answering) – you are not responsible for this material

**Reference: **(* = you are responsible for this material)

- *Sections 20.10.5-20.10.10 of the Deep Learning textbook.
- The Neural Autoregressive Distribution Estimator by Hugo Larochelle and Iain Murray (AISTAT2011)
- MADE: Masked Autoencoder for Distribution Estimation by Mathieu Germain, Karol Gregor, Iain Murray, Hugo Larochelle (ICML2015).
- Pixel Recurrent Neural Networks by Aaron van den Oord, Nal Kalchbrenner, Koray Kavukcuoglu (ICML2016)
- *Conditional Image Generation with PixelCNN Decoders by Aaron van den Oord, Nal Kalchbrenner, Oriol Vinyals, Lasse Espeholt, Alex Graves, Koray Kavukcuoglu (NIPS2016)
- Parallel Multiscale Autoregressive Density Estimation by Scott Reed, Aaron van den Oord, Nal Kalchbrenner, Sergio Gomez Colmenarejo, Ziyu Wang, Dan Belov and Nando de Freitas (arXiv:1703.03664, 2017)