# Boltzmann Encoded Adversarial Machines

arXiv: https://arxiv.org/abs/1804.08682 · HTML: https://mlsanity.com/paper/1804.08682/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2018.

## Built on by (most cited first, 3 of 3)

- [Text to Image Synthesis using Stacked Conditional Variational Autoencoders and Conditional Generative Adversarial Networks](https://mlsanity.com/paper/2207.03332/) (2022)
- [Elastic Interaction Energy-Based Generative Model: Approximation in Feature Space](https://mlsanity.com/paper/2303.10553/) (2023)
- [Understanding temperature tuning in energy-based models](https://mlsanity.com/paper/2512.09152/) (2025)

## Abstract (arXiv)

Restricted Boltzmann Machines (RBMs) are a class of generative neural network that are typically trained to maximize a log-likelihood objective function. We argue that likelihood-based training strategies may fail because the objective does not sufficiently penalize models that place a high probability in regions where the training data distribution has low probability. To overcome this problem, we introduce Boltzmann Encoded Adversarial Machines (BEAMs). A BEAM is an RBM trained against an adversary that uses the hidden layer activations of the RBM to discriminate between the training data and the probability distribution generated by the model. We present experiments demonstrating that BEAMs outperform RBMs and GANs on multiple benchmarks.