# Multi-layered Discriminative Restricted Boltzmann Machine with Untrained Probabilistic Layer

arXiv: https://arxiv.org/abs/2210.15434 · HTML: https://mlsanity.com/paper/2210.15434/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 22 references that are in the map.
- First posted on arXiv 27 Oct 2022.

## Builds on (most cited first, 4 of 4)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Restricted Boltzmann machine to determine the input weights for extreme learning machines](https://mlsanity.com/paper/1708.05376/) (2017)
- [Restricted Boltzmann Machine with Multivalued Hidden Variables: a model suppressing over-fitting](https://mlsanity.com/paper/1811.12587/) (2018)

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

- [Dataset-Free Weight-Initialization on Restricted Boltzmann Machine](https://mlsanity.com/paper/2409.07708/) (2024)

## Abstract (arXiv)

An extreme learning machine (ELM) is a three-layered feed-forward neural network having untrained parameters, which are randomly determined before training. Inspired by the idea of ELM, a probabilistic untrained layer called a probabilistic-ELM (PELM) layer is proposed, and it is combined with a discriminative restricted Boltzmann machine (DRBM), which is a probabilistic three-layered neural network for solving classification problems. The proposed model is obtained by stacking DRBM on the PELM layer. The resultant model (i.e., multi-layered DRBM (MDRBM)) forms a probabilistic four-layered neural network. In MDRBM, the parameters in the PELM layer can be determined using Gaussian-Bernoulli restricted Boltzmann machine. Owing to the PELM layer, MDRBM obtains a strong immunity against noise in inputs, which is one of the most important advantages of MDRBM. Numerical experiments using some benchmark datasets, MNIST, Fashion-MNIST, Urban Land Cover, and CIFAR-10, demonstrate that MDRBM is superior to other existing models, particularly, in terms of the noise-robustness property (or, in other words, the generalization property).