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 …

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 …

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

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 · Germain, Gregor & Larochelle $^{[2]}$, posted their findings in the paper MADE: Masked Autoencoder for Density Estimation. In my opion, they made a really elegant …

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

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 …

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 …

In this paper, we propose and analyze the use of nonparametric density methods to estimate the Jensen-Shannon divergence for matching unknown data distributions to known target …

use NN to "estimate a distribution" introduce simple modification for AutoEncoder(AE) NN key point : masks the AE parameters to respect "autoregressive constraints"