# Cooperative neural networks (CoNN): Exploiting prior independence structure for improved classification Source: [Cooperative neural networks (CoNN): Exploiting prior independence structure for improved classification]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:36:37.771Z Authors: Harsh Shrivastava, Eugene Bart, Bob Price, Hanjun Dai, Bo Dai, Srinivas Aluru Published: 2019-06-01T21:09:28.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Text Classification on 20 Newsgroups by-date (test)](): Text Classification; 20 Newsgroups by-date (test); Accuracy: 83.7. ## Original sources and implementation [Original paper]() [PDF]()