Summary of Data Distribution-based Curriculum Learning, by Shonal Chaudhry and Anuraganand Sharma
Data Distribution-based Curriculum Learning
by Shonal Chaudhry, Anuraganand Sharma
First submitted to arxiv on: 12 Feb 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 This paper proposes Data Distribution-based Curriculum Learning (DDCL), a novel approach to ordering training samples from easy to hard. DDCL uses data distribution to build a curriculum, and two scoring methods – Density and Point – are introduced to assign scores to samples. The authors evaluate DDCL on multiple datasets using neural networks, support vector machines, and random forests, showing improved average classification accuracy compared to standard evaluation. Additionally, analysis reveals faster convergence with DDCL over the no-curriculum method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows that how you order your training data can make a big difference in how well your classifier works. They came up with a new way to do this called Data Distribution-based Curriculum Learning (DDCL). DDCL looks at where each piece of data is located and uses that information to decide which ones are easiest to learn from first. The authors tested their method on many different datasets and found that it does better than just using all the data in one go. They also looked at how quickly the model learned and found that with DDCL, it learns faster too! |
Keywords
* Artificial intelligence * Classification * Curriculum learning
