# Semi-supervised learning with Bayesian Confidence Propagation Neural Network

arXiv: https://arxiv.org/abs/2106.15546 · HTML: https://mlsanity.com/paper/2106.15546/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 9 references that are in the map.
- First posted on arXiv 29 Jun 2021.

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

- [Semi-Supervised Learning with Deep Generative Models](https://mlsanity.com/paper/1406.5298/) (2014)
- [Semi-Supervised Learning with Ladder Networks](https://mlsanity.com/paper/1507.02672/) (2015)
- [Learning representations in Bayesian Confidence Propagation neural networks](https://mlsanity.com/paper/2003.12415/) (2020)
- [Brain-like approaches to unsupervised learning of hidden representations -- a comparative study](https://mlsanity.com/paper/2005.03476/) (2020)

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

- [Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors](https://mlsanity.com/paper/2205.10279/) (2022)

## Abstract (arXiv)

Learning internal representations from data using no or few labels is useful for machine learning research, as it allows using massive amounts of unlabeled data. In this work, we use the Bayesian Confidence Propagation Neural Network (BCPNN) model developed as a biologically plausible model of the cortex. Recent work has demonstrated that these networks can learn useful internal representations from data using local Bayesian-Hebbian learning rules. In this work, we show how such representations can be leveraged in a semi-supervised setting by introducing and comparing different classifiers. We also evaluate and compare such networks with other popular semi-supervised classifiers.