Summary of Robustifying and Boosting Training-free Neural Architecture Search, by Zhenfeng He et al.
Robustifying and Boosting Training-Free Neural Architecture Search
by Zhenfeng He, Yao Shu, Zhongxiang Dai, Bryan Kian Hsiang Low
First submitted to arxiv on: 12 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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 The proposed RoBoT algorithm is a novel approach to improve neural architecture search (NAS) by employing an optimized combination of existing training-free metrics, which can be used for diverse tasks. The algorithm first develops a robust and consistently better-performing metric through Bayesian optimization, and then applies greedy search to bridge the gap between training-free metrics and true architecture performances, leading to improved search performance. This approach has theoretical guarantees of expected performance improvements under mild conditions, which is supported by extensive experiments on various NAS benchmark tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RoBoT is a new way to find the best deep learning model without actually training it. The goal is to make this process more efficient and reliable. To do this, RoBoT combines different metrics that don’t require training, finds the best combination, and then uses that to search for the optimal model. This approach is guaranteed to perform better than previous methods under certain conditions. It was tested on various tasks and showed significant improvements. |
Keywords
* Artificial intelligence * Deep learning * Optimization
