Summary of Efficient Lifelong Model Evaluation in An Era Of Rapid Progress, by Ameya Prabhu et al.
Efficient Lifelong Model Evaluation in an Era of Rapid Progress
by Ameya Prabhu, Vishaal Udandarao, Philip Torr, Matthias Bethge, Adel Bibi, Samuel Albanie
First submitted to arxiv on: 29 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 paper proposes a solution to mitigate the overfitting problem in machine learning by introducing large-scale benchmarks called Lifelong Benchmarks, which contain millions of test samples. To efficiently evaluate models on these benchmarks, the authors introduce an approach called Sort & Search (S&S) that reuses previously evaluated models using dynamic programming algorithms. The S&S framework achieves highly-efficient approximate accuracy measurement, reducing compute cost from 180 GPU days to 5 GPU hours with low approximation error and memory cost. The paper also highlights issues with current accuracy prediction metrics, suggesting a need to move towards sample-level evaluation metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to fix a problem in machine learning where the same tests are used over and over again, which can make models too good at those specific tests but not as good on new data. To solve this, the authors create really big datasets with millions of test samples called Lifelong Benchmarks. Then, they come up with a way to quickly evaluate how well different models do on these datasets using an algorithm called Sort & Search (S&S). This makes it much faster and cheaper to test models, which is important for making progress in machine learning. |
Keywords
* Artificial intelligence * Machine learning * Overfitting
