Summary of A Scalable Algorithm For Active Learning, by Youguang Chen et al.
A Scalable Algorithm for Active Learning
by Youguang Chen, Zheyu Wen, George Biros
First submitted to arxiv on: 11 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 The proposed algorithm, FIRAL, is a deterministic active learning method that utilizes logistic regression for multiclass classification tasks. It has been shown to outperform existing approaches in terms of accuracy and robustness, while also providing theoretical performance guarantees. However, its scalability suffers when dealing with large datasets due to its high computational complexity. To address this challenge, an approximate algorithm is proposed, which reduces storage requirements and computational complexity while maintaining the same level of accuracy as FIRAL. A parallel implementation on GPUs is also presented, demonstrating strong and weak scaling tests on up to 12 GPUs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FIRAL is a new way for machines to learn from data without needing lots of labels. It’s good at picking the most important data points and making accurate predictions. But, it can get slow when dealing with really big datasets. To make it faster, we created a simpler version that still works well. We also made it work on many computers at the same time, which is helpful for big tasks. We tested FIRAL and its simplified version on several popular image recognition datasets, like MNIST and ImageNet, and they performed just as well. |
Keywords
» Artificial intelligence » Active learning » Classification » Logistic regression
