MADE: Masked Autoencoder for Distribution Estimation Mathieu Germain

There has been a lot of recent interest in designing neural network models to estimate a distribution from a set of examples. We introduce a simple modification for autoencoder neural networks that yields powerful generative models. Our method masks the autoencoder’s parameters to respect autoregressive constraints: each input is recon-structed only from previous inputs in a given or-dering. Constrained this way, the autoencoder outputs can be interpreted as a set of conditional probabilities, and their product, the full joint prob-ability. We can also train a single network that can decompose the joint probability in multiple different orderings. Our simple framework can be applied to multiple architectures, including deep ones. Vectorized implementations, such as on GPUs, are simple and fast. Experiments demon-strate that this approach is competitive with state-of-the-art tractable distribution estimators. At test time, the method is significantly faster and scales better than other autoregressive estimators. 1