Summary of Fast Yet Safe: Early-exiting with Risk Control, by Metod Jazbec et al.
Fast yet Safe: Early-Exiting with Risk Control
by Metod Jazbec, Alexander Timans, Tin Hadži Veljković, Kaspar Sakmann, Dan Zhang, Christian A. Naesseth, Eric Nalisnick
First submitted to arxiv on: 31 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 The paper proposes an innovative approach to scaling machine learning models by using early-exit neural networks (EENNs) with a risk control framework. EENNs accelerate inference by allowing intermediate layers to exit and produce a prediction early, but determining the optimal exit point is crucial. The authors adapt risk control frameworks to EENNs, enabling the model to exit only when the output meets user-specified performance goals. This approach yields significant computational savings on various vision and language tasks while maintaining desired performance levels. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps machine learning models work better by allowing them to stop early when they’re good enough. This “early-exit” trick speeds up processing, but it’s hard to know when to stop. The researchers used a special technique called risk control to figure out the right moment to exit and still get accurate results. They tested this on different tasks like image recognition and language understanding, showing that it can save a lot of computing power while keeping performance high. |
Keywords
» Artificial intelligence » Inference » Language understanding » Machine learning
