๐๐ฟ๐ฎ๐ฝ๐ต๐ก๐๐ฆ: ๐๐๐๐ผ๐บ๐ฎ๐๐ถ๐ป๐ด ๐๐ฟ๐ฎ๐ฝ๐ต ๐ก๐ฒ๐๐ฟ๐ฎ๐น ๐๐ฟ๐ฐ๐ต๐ถ๐๐ฒ๐ฐ๐๐๐ฟ๐ฒ ๐ฆ๐ฒ๐ฎ๐ฟ๐ฐ๐ต
Designing graph neural networks is hard. You spend hours testing different architectures. Most people rely on trial and error. This process takes time and lacks precision.
GraphNAS changes this. It uses reinforcement learning to find the best graph architectures for you.
The system works in three stages:
- Controller: An agent proposes new neural network structures.
- Evaluator: The system tests these structures on your data.
- Predictor: A model estimates performance to speed up the search.
This method removes the guesswork from your workflow. You get optimized models without manual tuning. It makes graph machine learning more efficient for everyone.
Read the full breakdown here: https://dev.to/paperium/graphnas-graph-neural-architecture-search-with-reinforcement-learning-52l5
Source: https://dev.to/paperium/graphnas-graph-neural-architecture-search-with-reinforcement-learning-52l5
Optional learning community: https://t.me/GyaanSetuAi