Papers › MADE: Masked Autoencoder for Distribution Estimation
MADE: Masked Autoencoder for Distribution Estimation
Mathieu Germain, Karol Gregor, Iain Murray, Hugo Larochelle
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 reconstructed only from previous inputs in a given ordering. Constrained this way, the autoencoder outputs can be interpreted as a set of conditional probabilities, and their product, the full joint probability. 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 demonstrate 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.
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Code
Syntology Ran 5 of 9 code samples harvested from 4 repositories linked to this paper; 4 have no recorded run. Of those that ran: 2 ran · honoured contract; 1 ran · our draft was wrong; 2 ran · fixture could not drive it.
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Code Syntology ran Syntology
9 samples harvested; 5 ran; 2 honoured the contract we drafted; 4 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Density Estimation | UCI GAS | MADE MoG | Log-likelihood | 8.47 | #4 of 5 | Archive leaderboard | report |
| Image Generation | Binarized MNIST | MADE 2hl (32 orders) | nats | 86.64 | #9 of 10 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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