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Reflex Activity

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Related Experiment Video

Updated: May 1, 2026

Topographical Estimation of Visual Population Receptive Fields by fMRI

Topographical Estimation of Visual Population Receptive Fields by fMRI

Published on: February 4, 2015

RBFleX-NAS: Training-Free Neural Architecture Search Using Radial Basis Function Kernel and Hyperparameter Detection.

Tomomasa Yamasaki, Zhehui Wang, Tao Luo

IEEE Transactions on Neural Networks and Learning Systems

|April 9, 2025

Summary

This summary is machine-generated.

RBFleX-NAS, a novel training-free neural architecture search (NAS) method, accurately predicts network performance using radial basis function kernels. It excels in activation function exploration and achieves superior accuracy with reduced search time.

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Area of Science:

Artificial Intelligence
Machine Learning
Computer Science

Background:

Neural Architecture Search (NAS) automates neural network design but conventionally requires extensive training.
Training-free NAS methods accelerate evaluation but often lack precision in performance prediction and activation function exploration.
Existing methods struggle to accurately distinguish well-performing from poorly performing networks, leading to suboptimal results.

Purpose of the Study:

To introduce RBFleX-NAS, a novel training-free NAS framework designed to overcome the limitations of current methods.
To improve the accuracy of performance prediction and enhance the effectiveness of activation function exploration in NAS.
To reduce the search time and computational resources required for NAS.

Main Methods:

Proposes RBFleX-NAS, a training-free NAS framework utilizing a radial basis function (RBF) kernel.
Incorporates activation outputs and input features of the last layer into the search process.
Introduces a detection algorithm for identifying optimal hyperparameters using activation outputs and feature maps.

Main Results:

RBFleX-NAS significantly outperforms state-of-the-art training-free NAS methods in top-one accuracy on NAS-Bench-201 and NAS-Bench-SSS.
Achieves high accuracy with a notably short search time.
Demonstrates superior Kendall correlation compared to layer-based training-free NAS algorithms.
Successfully identifies the best-performing network in the new NAFBee benchmark, excelling in activation function search.

Conclusions:

RBFleX-NAS offers a more accurate and efficient approach to training-free neural architecture search.
The framework effectively addresses limitations in performance prediction and activation function exploration.
RBFleX-NAS provides a significant advantage for optimizing neural network architectures, especially in complex search spaces.