Summary of Boosting Deep Ensembles with Learning Rate Tuning, by Hongpeng Jin et al.
Boosting Deep Ensembles with Learning Rate Tuning
by Hongpeng Jin, Yanzhao Wu
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 novel framework called LREnsemble is introduced for boosting deep learning system performance by effectively leveraging effective learning rate tuning and deep ensemble techniques. This paper presents three original contributions: first, it shows that different learning rate policies can produce diverse Deep Neural Networks (DNNs) suitable as base models for ensembles; second, it leverages various ensemble selection algorithms to identify high-quality ensembles with significant accuracy improvements over the best single model; and third, it proposes LREnsemble, a framework combining LR tuning and deep ensemble techniques. The method is evaluated on multiple benchmark datasets, achieving up to 2.34% accuracy improvements over well-optimized baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LREnsemble is a new way to make deep learning better. Right now, people have to try different settings many times to find the best one. But even if they do, they only use that setting and miss out on opportunities to make their model even better. This paper shows how to use this process to create a team of models (called an ensemble) that work together to make predictions. The team is stronger than any single model alone, and it can make more accurate predictions. The researchers tested LREnsemble on several datasets and found that it can improve accuracy by up to 2.34%. |
Keywords
» Artificial intelligence » Boosting » Deep learning
