# Cooperative neural networks (CoNN): Exploiting prior independence structure for improved classification

arXiv: https://arxiv.org/abs/1906.00291 · HTML: https://mlsanity.com/paper/1906.00291/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jun 2019.

## Built on by (most cited first, 6 of 6)

- [Neural Graphical Models](https://mlsanity.com/paper/2210.00453/) (2022)
- [Methods for Recovering Conditional Independence Graphs: A Survey](https://mlsanity.com/paper/2211.06829/) (2022)
- [uGLAD: Sparse graph recovery by optimizing deep unrolled networks](https://mlsanity.com/paper/2205.11610/) (2022)
- [Neural Graph Revealers](https://mlsanity.com/paper/2302.13582/) (2023)
- [An Explainable Probabilistic Classifier for Categorical Data Inspired to Quantum Physics](https://mlsanity.com/paper/2105.13988/) (2021)
- [Obstacle-aware Gaussian Process Regression](https://mlsanity.com/paper/2412.06160/) (2024)

## Abstract (arXiv)

We propose a new approach, called cooperative neural networks (CoNN), which uses a set of cooperatively trained neural networks to capture latent representations that exploit prior given independence structure. The model is more flexible than traditional graphical models based on exponential family distributions, but incorporates more domain specific prior structure than traditional deep networks or variational autoencoders. The framework is very general and can be used to exploit the independence structure of any graphical model. We illustrate the technique by showing that we can transfer the independence structure of the popular Latent Dirichlet Allocation (LDA) model to a cooperative neural network, CoNN-sLDA. Empirical evaluation of CoNN-sLDA on supervised text classification tasks demonstrates that the theoretical advantages of prior independence structure can be realized in practice -we demonstrate a 23% reduction in error on the challenging MultiSent data set compared to state-of-the-art.