# DropNeuron: Simplifying the Structure of Deep Neural Networks

arXiv: https://arxiv.org/abs/1606.07326 · HTML: https://mlsanity.com/paper/1606.07326/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jun 2016.

## Built on by (most cited first, 11 of 11)

- [Topology and Geometry of the Learning Space of ReLU Networks: Connectivity and Singularities](https://mlsanity.com/paper/2602.00693/) (2026)
- [On the Compression of Neural Networks Using ℓ0-Norm Regularization and Weight Pruning](https://mlsanity.com/paper/2109.05075/) (2021)
- [BLaST: High Performance Inference and Pretraining using BLock Sparse Transformers](https://mlsanity.com/paper/2507.03117/) (2025)
- [What to Prune and What Not to Prune at Initialization](https://mlsanity.com/paper/2209.02201/) (2022)
- [Training Lightweight Graph Convolutional Networks with Phase-field Models](https://mlsanity.com/paper/2212.09415/) (2022)
- [One-Shot Multi-Rate Pruning of Graph Convolutional Networks](https://mlsanity.com/paper/2312.17615/) (2023)
- [On GNN explanability with activation rules](https://mlsanity.com/paper/2406.11594/) (2024)
- [Designing Semi-Structured Pruning of Graph Convolutional Networks for Skeleton-based Recognition](https://mlsanity.com/paper/2412.11813/) (2024)
- [Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition](https://mlsanity.com/paper/2412.12887/) (2024)
- [Downsized and Compromised?: Assessing the Faithfulness of Model Compression](https://mlsanity.com/paper/2510.06125/) (2025)
- [Layerwise Decoupling for Stable Structured Sparsification of Fully Connected Layers](https://mlsanity.com/paper/2609.21126/) (2026)

## Abstract (arXiv)

Deep learning using multi-layer neural networks (NNs) architecture manifests superb power in modern machine learning systems. The trained Deep Neural Networks (DNNs) are typically large. The question we would like to address is whether it is possible to simplify the NN during training process to achieve a reasonable performance within an acceptable computational time. We presented a novel approach of optimising a deep neural network through regularisation of net- work architecture. We proposed regularisers which support a simple mechanism of dropping neurons during a network training process. The method supports the construction of a simpler deep neural networks with compatible performance with its simplified version. As a proof of concept, we evaluate the proposed method with examples including sparse linear regression, deep autoencoder and convolutional neural network. The valuations demonstrate excellent performance. The code for this work can be found in http://www.github.com/panweihit/DropNeuron