# Mediated Experts for Deep Convolutional Networks

arXiv: https://arxiv.org/abs/1511.06072 · HTML: https://mlsanity.com/paper/1511.06072/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Nov 2015.

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

- [Graph Neural Network for Traffic Forecasting: A Survey](https://mlsanity.com/paper/2101.11174/) (2021)
- [A Primer on Bayesian Neural Networks: Review and Debates](https://mlsanity.com/paper/2309.16314/) (2023)
- [Mixture of Experts in Large Language Models](https://mlsanity.com/paper/2507.11181/) (2025)
- [Deep Stable neural networks: large-width asymptotics and convergence rates](https://mlsanity.com/paper/2108.02316/) (2021)
- [Multi-model Ensemble Analysis with Neural Network Gaussian Processes](https://mlsanity.com/paper/2202.04152/) (2022)
- [Graph Construction using Principal Axis Trees for Simple Graph Convolution](https://mlsanity.com/paper/2302.12000/) (2023)

## Abstract (arXiv)

We present a new supervised architecture termed Mediated Mixture-of-Experts (MMoE) that allows us to improve classification accuracy of Deep Convolutional Networks (DCN). Our architecture achieves this with the help of expert networks: A network is trained on a disjoint subset of a given dataset and then run in parallel to other experts during deployment. A mediator is employed if experts contradict each other. This allows our framework to naturally support incremental learning, as adding new classes requires (re-)training of the new expert only. We also propose two measures to control computational complexity: An early-stopping mechanism halts experts that have low confidence in their prediction. The system allows to trade-off accuracy and complexity without further retraining. We also suggest to share low-level convolutional layers between experts in an effort to avoid computation of a near-duplicate feature set. We evaluate our system on a popular dataset and report improved accuracy compared to a single model of same configuration.