Summary of Comparison Of Machine Learning Approaches For Classifying Spinodal Events, by Ashwini Malviya et al.
Comparison of Machine Learning Approaches for Classifying Spinodal Events
by Ashwini Malviya, Sparsh Mittal
First submitted to arxiv on: 13 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: High Energy Physics – Experiment (hep-ex); Data Analysis, Statistics and Probability (physics.data-an)
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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 A comparison of deep learning models is presented in this study, focusing on classifying the spinodal dataset. State-of-the-art models such as MobileViT, NAT, EfficientNet, and CNN are evaluated alongside ensemble models like majority voting and AdaBoost. The research also explores the dataset in a transformed color space. Notably, NAT and MobileViT outperform other models, achieving high accuracy, AUC, and F1 scores on both training and testing data. For instance, NAT achieves an accuracy of 94.65%, AUC of 0.98, and F1 score of 0.94. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study compares different deep learning models to see which one works best for classifying a certain dataset called spinodal. The researchers tested popular models like MobileViT, NAT, EfficientNet, and CNN, as well as some combined models. They also looked at the dataset in a new way by changing its color space. The results show that two models, NAT and MobileViT, are the best, getting high scores for accuracy, AUC, and F1 score on both training and testing data. |
Keywords
» Artificial intelligence » Auc » Cnn » Deep learning » F1 score
