Summary of Insights From the Use Of Previously Unseen Neural Architecture Search Datasets, by Rob Geada et al.
Insights from the Use of Previously Unseen Neural Architecture Search Datasets
by Rob Geada, David Towers, Matthew Forshaw, Amir Atapour-Abarghouei, A. Stephen McGough
First submitted to arxiv on: 2 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 paper discusses the limitations of relying solely on Neural Architecture Search (NAS) for selecting the most suitable neural network architecture, as it often requires expert knowledge to identify the best-performing model. To overcome this issue, the authors propose eight new datasets, designed specifically for NAS Challenges, which aim to address problems in NAS development and encourage researchers to develop models that generalize well to unseen data. The authors present experimentation results using standard deep learning methods and the top-performing submissions from challenge participants. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a bunch of different neural networks that can solve a problem, but each one works slightly differently. This makes it hard for non-experts to figure out which network is best. Neural Architecture Search (NAS) tries to solve this by automatically finding the best architecture. However, current NAS methods only work well on a small set of datasets and don’t represent real-world problems. To fix this, researchers created eight new datasets that are designed specifically for testing NAS methods. These datasets aim to challenge developers to create models that can perform well even when they haven’t seen the data before. |
Keywords
* Artificial intelligence * Deep learning * Neural network
