Summary of Flowerformer: Empowering Neural Architecture Encoding Using a Flow-aware Graph Transformer, by Dongyeong Hwang et al.
FlowerFormer: Empowering Neural Architecture Encoding using a Flow-aware Graph Transformer
by Dongyeong Hwang, Hyunju Kim, Sunwoo Kim, Kijung Shin
First submitted to arxiv on: 19 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 introduces FlowerFormer, a novel graph transformer that encodes neural architectures for efficient performance estimation. By incorporating information flows within an architecture, FlowerFormer outperforms existing methods in estimating performances of computer vision models, graph neural networks, and auto speech recognition models. The architecture consists of bidirectional asynchronous message passing and global attention built on flow-based masking. Experimental results demonstrate the superiority of FlowerFormer over state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Researchers have been trying to find a way to quickly estimate how well a specific type of artificial intelligence (AI) will perform without having to train or test it first. They’ve made progress by treating an AI’s architecture like a graph, which helps them make better predictions. The new method, called FlowerFormer, is even more effective and can be used for different types of AI models. |
Keywords
* Artificial intelligence * Attention * Transformer
