Summary of Scalable Reinforcement Learning-based Neural Architecture Search, by Amber Cassimon et al.
Scalable Reinforcement Learning-based Neural Architecture Search
by Amber Cassimon, Siegfried Mercelis, Kevin Mets
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 This paper presents a novel reinforcement learning-based solution for neural architecture search (NAS). Unlike traditional NAS methods, this approach learns to search for good architectures rather than returning a single optimal one. The authors evaluate their method on two benchmark datasets, NAS-Bench-101 and NAS-Bench-301, and compare it to strong baselines such as local search and random search. The results show that the reinforcement learning agent demonstrates strong scalability in terms of the size of the search space, but limited robustness to hyperparameter changes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to find good designs for artificial neural networks. Instead of just picking one design that works best, this method lets an “agent” learn to search for good designs on its own. The researchers tested their approach on two big datasets and compared it to other methods that are already known to work well. They found that their agent is very good at finding good designs when the problem gets bigger, but it’s not as good at handling small changes in how the design is created. |
Keywords
» Artificial intelligence » Hyperparameter » Reinforcement learning
