Summary of Towards Fundamentally Scalable Model Selection: Asymptotically Fast Update and Selection, by Wenxiao Wang et al.
Towards Fundamentally Scalable Model Selection: Asymptotically Fast Update and Selection
by Wenxiao Wang, Weiming Zhuang, Lingjuan Lyu
First submitted to arxiv on: 11 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); 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 This paper focuses on developing a scalable model selection scheme, which can efficiently support two key operations: updating the pool of candidate models and selecting highly performing models for a given task. The proposed solution, isolated model embedding, achieves asymptotically fast update and selection complexity, making it suitable for large-scale applications. This family of model selection schemes also exhibits desirable properties for real-world use cases. To demonstrate its effectiveness, the authors present Standardized Embedder, an empirical realization of isolated model embedding, which selects representations from a pool of 100 pre-trained vision models for classification tasks. The results show that this approach is capable of selecting models with competitive performances. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to solve a big problem in machine learning. They want to find the best way to choose between many different models that can do a task, like recognizing pictures. Right now, finding the right model takes too long or is too hard. The researchers came up with a new idea called isolated model embedding, which makes it faster and easier to update the list of models and pick the best ones. They even tested this idea by using it to choose between 100 different picture-recognizing models and saw that it works pretty well. |
Keywords
» Artificial intelligence » Classification » Embedding » Machine learning
