Summary of Einspace: Searching For Neural Architectures From Fundamental Operations, by Linus Ericsson et al.
einspace: Searching for Neural Architectures from Fundamental Operations
by Linus Ericsson, Miguel Espinosa, Chenhongyi Yang, Antreas Antoniou, Amos Storkey, Shay B. Cohen, Steven McDonagh, Elliot J. Crowley
First submitted to arxiv on: 31 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML)
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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 proposes a novel approach to neural architecture search (NAS) that aims to find more fundamental design shifts in network architectures. The current state-of-the-art NAS methods often produce incremental improvements, but not revolutionary changes. To achieve this, the authors introduce einspace, a parameterised probabilistic context-free grammar-based search space that can model various network operations, including convolutions and attention components. This versatile search space allows for the discovery of new architectures as well as improvements on existing ones. The authors conduct experiments on the Unseen NAS datasets, showing that competitive architectures can be obtained by searching from scratch or initializing with strong baselines. The results demonstrate large improvements when using strategic search initialisation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about finding better ways to design artificial intelligence networks. Right now, these networks are mostly similar, but researchers want to find new and more effective designs. To do this, the authors introduce a new way of searching for network architectures that can create more innovative designs. This approach uses a special type of grammar rule that can generate many different types of neural networks. The authors test their approach on some datasets and show that it can lead to big improvements in performance. |
Keywords
» Artificial intelligence » Attention
