Summary of Bayesian Optimization For Hyperparameters Tuning in Neural Networks, by Gabriele Onorato
Bayesian Optimization for Hyperparameters Tuning in Neural Networks
by Gabriele Onorato
First submitted to arxiv on: 29 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Optimization and Control (math.OC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study explores the application of Bayesian Optimization (BO) to optimize hyperparameters for Convolutional Neural Networks (CNNs) in image classification tasks. By leveraging Gaussian Process regression and acquisition functions like Upper Confidence Bound (UCB) and Expected Improvement (EI), BO efficiently reduces the number of hyperparameter tuning trials while achieving competitive model performance. Using frameworks like Ax and BOTorch, this work demonstrates the potential of BO in automating neural network tuning, contributing to improved accuracy and computational efficiency in machine learning pipelines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary BO helps optimize CNNs for image classification tasks by reducing the number of hyperparameter tuning trials. This approach uses Gaussian Process regression and acquisition functions to find optimal configurations. Results show that BO effectively balances exploration and exploitation, rapidly converging towards optimal settings for CNN architectures. |
Keywords
» Artificial intelligence » Cnn » Hyperparameter » Image classification » Machine learning » Neural network » Optimization » Regression
