Summary of A Survey on Neural Architecture Search Based on Reinforcement Learning, by Wenzhu Shao
A Survey on Neural Architecture Search Based on Reinforcement Learning
by Wenzhu Shao
First submitted to arxiv on: 26 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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 Medium Difficulty summary: The paper explores Neural Architecture Search (NAS), a technique that automates the process of finding optimal neural network architectures for specific tasks. NAS is built upon Hyperparameter Optimization, which automates the search for optimal hyperparameters. This research aims to understand the development of NAS and its applications in reinforcement learning, focusing on improvements and variants that enable more complex structures and efficient resource utilization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: Imagine a super smart computer that can design its own architecture to solve problems. That’s what Neural Architecture Search (NAS) is all about! Currently, people have to manually adjust settings for different tasks, which takes a lot of time and effort. The goal of this research is to develop an algorithm that can automatically find the best network structure for specific jobs. This way, computers can work more efficiently and effectively. |
Keywords
» Artificial intelligence » Hyperparameter » Neural network » Optimization » Reinforcement learning
