Summary of Multi-conditioned Graph Diffusion For Neural Architecture Search, by Rohan Asthana et al.
Multi-conditioned Graph Diffusion for Neural Architecture Search
by Rohan Asthana, Joschua Conrad, Youssef Dawoud, Maurits Ortmanns, Vasileios Belagiannis
First submitted to arxiv on: 9 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 This paper presents a graph diffusion-based neural architecture search (NAS) approach that utilizes discrete conditional graph diffusion processes to generate high-performing neural network architectures. The proposed method incorporates a multi-conditioned classifier-free guidance approach to jointly impose constraints such as high accuracy and low hardware latency, making it completely differentiable and requiring only a single model training. The authors evaluate their method on six standard benchmarks, demonstrating promising results with novel and unique architectures generated at a fast speed of less than 0.2 seconds per architecture. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses computers to automatically design the best neural networks for tasks like image recognition. It’s a new way to find good designs by using graph diffusion, which is a type of computer process that helps generate different network architectures. The method also includes some rules to make sure the designed networks are both accurate and fast. The authors tested their approach on several well-known datasets and found that it could come up with good solutions quickly. |
Keywords
* Artificial intelligence * Diffusion * Neural network
