Summary of Accelerating Deep Learning with Fixed Time Budget, by Muhammad Asif Khan et al.
Accelerating Deep Learning with Fixed Time Budget
by Muhammad Asif Khan, Ridha Hamila, Hamid Menouar
First submitted to arxiv on: 3 Oct 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 proposed technique for training arbitrary deep learning models within fixed time constraints utilizes sample importance and dynamic ranking, providing an effective solution for edge-based learning and federated learning applications. The method is evaluated in both classification and regression tasks in computer vision, demonstrating clear gains achieved by the proposed approach in improving the learning performance of various state-of-the-art deep learning models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new way to train deep learning models that uses sample importance and dynamic ranking to speed up training time. This can be important for applications where data is limited or computing power is restricted, like edge-based learning or federated learning. The method is tested on computer vision tasks and shows promising results. |
Keywords
» Artificial intelligence » Classification » Deep learning » Federated learning » Regression
