# Efficient Hyperparameter Importance Assessment for CNNs

arXiv: https://arxiv.org/abs/2410.08920 · HTML: https://mlsanity.com/paper/2410.08920/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 19 references that are in the map.
- First posted on arXiv 11 Oct 2024.

## Builds on (most cited first, 2 of 2)

- [A Tutorial on Bayesian Optimization](https://mlsanity.com/paper/1807.02811/) (2018)
- [BOHB: Robust and Efficient Hyperparameter Optimization at Scale](https://mlsanity.com/paper/1807.01774/) (2018)

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

- [Grouped Sequential Optimization Strategy -- the Application of Hyperparameter Importance Assessment in Deep Learning](https://mlsanity.com/paper/2503.05106/) (2025)
- [Importance-Aware Scheduling for High-Dimensional Hyperparameter Optimization](https://mlsanity.com/paper/2606.10068/) (2026)
- [On Hyperparameter Tuning on the Test Set](https://mlsanity.com/paper/2610.05902/) (2026)

## Abstract (arXiv)

Hyperparameter selection is an essential aspect of the machine learning pipeline, profoundly impacting models' robustness, stability, and generalization capabilities. Given the complex hyperparameter spaces associated with Neural Networks and the constraints of computational resources and time, optimizing all hyperparameters becomes impractical. In this context, leveraging hyperparameter importance assessment (HIA) can provide valuable guidance by narrowing down the search space. This enables machine learning practitioners to focus their optimization efforts on the hyperparameters with the most significant impact on model performance while conserving time and resources. This paper aims to quantify the importance weights of some hyperparameters in Convolutional Neural Networks (CNNs) with an algorithm called N-RReliefF, laying the groundwork for applying HIA methodologies in the Deep Learning field. We conduct an extensive study by training over ten thousand CNN models across ten popular image classification datasets, thereby acquiring a comprehensive dataset containing hyperparameter configuration instances and their corresponding performance metrics. It is demonstrated that among the investigated hyperparameters, the top five important hyperparameters of the CNN model are the number of convolutional layers, learning rate, dropout rate, optimizer and epoch.