Summary of Swift Sampler: Efficient Learning Of Sampler by 10 Parameters, By Jiawei Yao et al.
Swift Sampler: Efficient Learning of Sampler by 10 Parameters
by Jiawei Yao, Chuming Li, Canran Xiao
First submitted to arxiv on: 8 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 an automatic data sampler search algorithm called Swift Sampler (SS) that efficiently explores effective samplers for training deep learning models. The SS algorithm uses a novel formulation to map a sampler to a low-dimensional hyperparameter space, allowing it to quickly evaluate the quality of a sampler. This approach reduces computational expense and enables SS to be applied to large-scale datasets. Experimental results on various tasks demonstrate that powered sampling can achieve significant improvements (e.g., 1.5% on ImageNet) and transfer across different neural networks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates an automatic way to find the best way to choose training data for deep learning models. This is important because choosing the right data helps the model learn well. The authors developed a new method called Swift Sampler that can quickly test many different ways of choosing data and pick the best one. This method works by simplifying the complex problem of finding good data samplers into a smaller, more manageable space. This makes it faster and easier to find a good sampler. The authors tested their method on many different datasets and found that it worked well, improving performance by up to 1.5% in some cases. |
Keywords
» Artificial intelligence » Deep learning » Hyperparameter
