Summary of Occam: Towards Cost-efficient and Accuracy-aware Classification Inference, by Dujian Ding et al.
OCCAM: Towards Cost-Efficient and Accuracy-Aware Classification Inference
by Dujian Ding, Bicheng Xu, Laks V.S. Lakshmanan
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 A novel approach to optimize classification queries is proposed in this paper, which leverages the strengths of different machine learning models while considering their inference costs. The OCCAM framework assigns the best classifier for each query based on user-specified cost budgets and maximizes aggregated accuracy. The approach uses an unbiased and low-variance accuracy estimator and solves an integer linear programming problem to find the optimal solution. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us choose the right machine learning model for specific tasks, like recognizing patterns in medical images or understanding text. By using different models for different tasks, we can get better results while also saving time and resources. The approach is tested on real-world datasets and shows that it can reduce costs by 40% without sacrificing too much accuracy. |
Keywords
* Artificial intelligence * Classification * Inference * Machine learning
