# Learning Boltzmann Machine with EM-like Method

arXiv: https://arxiv.org/abs/1609.01840 · HTML: https://mlsanity.com/paper/1609.01840/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Sep 2016.

## Built on by (most cited first, 1 of 1)

- [Reviewing Evolution of Learning Functions and Semantic Information Measures for Understanding Deep Learning](https://mlsanity.com/paper/2305.14397/) (2023)

## Abstract (arXiv)

We propose an expectation-maximization-like(EMlike) method to train Boltzmann machine with unconstrained connectivity. It adopts Monte Carlo approximation in the E-step, and replaces the intractable likelihood objective with efficiently computed objectives or directly approximates the gradient of likelihood objective in the M-step. The EM-like method is a modification of alternating minimization. We prove that EM-like method will be the exactly same with contrastive divergence in restricted Boltzmann machine if the M-step of this method adopts special approximation. We also propose a new measure to assess the performance of Boltzmann machine as generative models of data, and its computational complexity is O(Rmn). Finally, we demonstrate the performance of EM-like method using numerical experiments.