Feb 12, 2015 · We introduce a simple modification for autoencoder neural networks that yields powerful generative models. Our method masks the autoencoder's parameters to respect …

We introduce a simple modification for autoencoder neural networks that yields powerful generative models. Our method masks the autoencoder’s parameters to respect …

Jul 6, 2015 · We introduce a simple modification for autoencoder neural networks that yields powerful generative models. Our method masks the autoencoder's parameters to respect …

We introduce a simple modification for autoencoder neural networks that yields powerful generative models. Our method masks the autoencoder’s parameters to respect …

MADE: Masked Autoencoder for Distribution Estimation. Paper on arXiv and at ICML2015. This repository is for the original Theano implementation. If you are looking for a PyTorch …

Masked autoencoder for distribution estimation (MADE) is a well-structured density estimator, which alters a simple autoencoder by setting a set of masks on its connections to satisfy the …

The resulting Masked Autoencoder Distribution Estimator (MADE) preserves the efficiency of a single pass through a regular autoencoder. Implementation on a GPU is straightforward, …

Oct 25, 2019 · 2. Distribution estimation. If we bully the autoencoders just a bit more, by also blinding them partially, we can actually make them learn $P(x)$, i.e. the distribution of $x$. …

Jul 28, 2020 · This property is formally referred to as “autoregression” (dependence on itself), and is implemented in MADE by introducing masks for the weights of the neural network that is …

PyTorch implementation of the Masked Autoencoder for Distribution Estimation (MADE) [1]. The implemented model supports random ordering of the inputs for order-agnostic training.