Summary of Fastbo: Fast Hpo and Nas with Adaptive Fidelity Identification, by Jiantong Jiang and Ajmal Mian
FastBO: Fast HPO and NAS with Adaptive Fidelity Identification
by Jiantong Jiang, Ajmal Mian
First submitted to arxiv on: 1 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 multi-fidelity Bayesian optimization (BO) method called FastBO is proposed for hyperparameter optimization in machine learning. This approach efficiently determines the fidelity for each hyperparameter configuration, enabling strong performance. The authors extend BO into the multi-fidelity setting by introducing efficient point and saturation points, allowing any single-fidelity method to be adapted to this setting. The proposed method, FastBO, showcases its generality and applicability in achieving state-of-the-art results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning researchers are working on ways to create better models for tasks like image recognition and speech recognition. A new approach called FastBO helps find the best combinations of parameters to use in these models. It’s a way to make sure that the right amount of data is used at each step, which can help create even better models. |
Keywords
» Artificial intelligence » Hyperparameter » Machine learning » Optimization
