Imagine being able to evaluate the performance of 100 neural networks in just 8 seconds, without any training. Sounds too good to be true? Well, it’s now a reality thanks to RBFleX-NAS, a novel approach to Neural Architecture Search (NAS) that eliminates the need for extensive training.
By leveraging Radial Basis Function (RBF) kernels, RBFleX-NAS can efficiently evaluate network performance, providing accurate predictions and optimized architectures for specific workloads. This innovative framework has already shown superior performance in benchmarks like NAS-Bench-201 and NAS-Bench-SSS.
But what makes RBFleX-NAS so effective? For starters, it incorporates an advanced detection algorithm that identifies the best hyperparameters using outputs from activation functions and last-layer input features. Additionally, the framework extends activation function designs through NAFBee, a new benchmark that allows for diverse exploration of activation functions.
The implications of RBFleX-NAS are significant, as it paves the way for faster and more efficient NAS. With the ability to evaluate networks quickly and accurately, researchers and developers can focus on creating better models that drive real-world impact.
If you’re interested in learning more, be sure to check out the paper and GitHub repository linked below.
Paper: https://ieeexplore.ieee.org/document/10959729
GitHub: https://github.com/tomomasayamasaki/RBFleX-NAS