# Confident magnitude-based neural network pruning

arXiv: https://arxiv.org/abs/2408.04759 · HTML: https://mlsanity.com/paper/2408.04759/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 28 references that are in the map.
- First posted on arXiv 8 Aug 2024.

## Builds on (most cited first, 20 of 20)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Learning both Weights and Connections for Efficient Neural Networks](https://mlsanity.com/paper/1506.02626/) (2015)
- [SNIP: Single-shot Network Pruning based on Connection Sensitivity](https://mlsanity.com/paper/1810.02340/) (2018)
- [Rethinking the Value of Network Pruning](https://mlsanity.com/paper/1810.05270/) (2018)
- [What is the State of Neural Network Pruning?](https://mlsanity.com/paper/2003.03033/) (2020)
- [Confident Adaptive Language Modeling](https://mlsanity.com/paper/2207.07061/) (2022)
- [Distribution-Free, Risk-Controlling Prediction Sets](https://mlsanity.com/paper/2101.02703/) (2021)
- [Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control](https://mlsanity.com/paper/2110.01052/) (2021)
- [A Survey on Deep Neural Network Pruning-Taxonomy, Comparison, Analysis, and Recommendations](https://mlsanity.com/paper/2308.06767/) (2023)
- [Conformal Language Modeling](https://mlsanity.com/paper/2306.10193/) (2023)
- [A multi-centre polyp detection and segmentation dataset for generalisability assessment](https://mlsanity.com/paper/2106.04463/) (2021)
- [Methods for Pruning Deep Neural Networks](https://mlsanity.com/paper/2011.00241/) (2020)
- [Fast as CHITA: Neural Network Pruning with Combinatorial Optimization](https://mlsanity.com/paper/2302.14623/) (2023)
- [Fast yet Safe: Early-Exiting with Risk Control](https://mlsanity.com/paper/2405.20915/) (2024)
- [Pruning Deep Neural Networks from a Sparsity Perspective](https://mlsanity.com/paper/2302.05601/) (2023)
- [Efficiently Controlling Multiple Risks with Pareto Testing](https://mlsanity.com/paper/2210.07913/) (2022)
- [Junk DNA Hypothesis: Pruning Small Pre-Trained Weights Irreversibly and Monotonically Impairs "Difficult" Downstream Tasks in LLMs](https://mlsanity.com/paper/2310.02277/) (2023)
- [Early Time Classification with Accumulated Accuracy Gap Control](https://mlsanity.com/paper/2402.00857/) (2024)
- [Risk-Controlling Model Selection via Guided Bayesian Optimization](https://mlsanity.com/paper/2312.01692/) (2023)
- [A distribution-free valid p-value for finite samples of bounded random variables](https://mlsanity.com/paper/2405.08975/) (2024)

## Built on by (most cited first, 1 of 1)

- [Pruning and Quantization Impact on Graph Neural Networks](https://mlsanity.com/paper/2510.22058/) (2025)

## Abstract (arXiv)

Pruning neural networks has proven to be a successful approach to increase the efficiency and reduce the memory storage of deep learning models without compromising performance. Previous literature has shown that it is possible to achieve a sizable reduction in the number of parameters of a deep neural network without deteriorating its predictive capacity in one-shot pruning regimes. Our work builds beyond this background in order to provide rigorous uncertainty quantification for pruning neural networks reliably, which has not been addressed to a great extent in previous literature focusing on pruning methods in computer vision settings. We leverage recent techniques on distribution-free uncertainty quantification to provide finite-sample statistical guarantees to compress deep neural networks, while maintaining high performance. Moreover, this work presents experiments in computer vision tasks to illustrate how uncertainty-aware pruning is a useful approach to deploy sparse neural networks safely.