# Differentiable Sparsification for Deep Neural Networks

arXiv: https://arxiv.org/abs/1910.03201 · HTML: https://mlsanity.com/paper/1910.03201/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Oct 2019.

## Built on by (most cited first, 3 of 3)

- [Towards Improving the Consistency, Efficiency, and Flexibility of Differentiable Neural Architecture Search](https://mlsanity.com/paper/2101.11342/) (2021)
- [Neural Network Pruning by Gradient Descent](https://mlsanity.com/paper/2311.12526/) (2023)
- [Learning a Consensus Sub-Network with Polarization Regularization and One Pass Training](https://mlsanity.com/paper/2302.10798/) (2023)

## Abstract (arXiv)

Deep neural networks have significantly alleviated the burden of feature engineering, but comparable efforts are now required to determine effective architectures for these networks. Furthermore, as network sizes have become excessively large, a substantial amount of resources is invested in reducing their sizes. These challenges can be effectively addressed through the sparsification of over-complete models. In this study, we propose a fully differentiable sparsification method for deep neural networks, which can zero out unimportant parameters by directly optimizing a regularized objective function with stochastic gradient descent. Consequently, the proposed method can learn both the sparsified structure and weights of a network in an end-to-end manner. It can be directly applied to various modern deep neural networks and requires minimal modification to the training process. To the best of our knowledge, this is the first fully differentiable sparsification method.