AI BriefingLG AI Research: Neural Architecture Search (NAS) Review
Key point
This reviews the concept of Neural Architecture Search (NAS), which automatically searches for optimal neural network architectures, along with cost-reduction techniques.
Details
Neural Architecture Search (NAS) is a field of AutoML that automatically searches for neural network architectures optimized for a specific goal. Previously, researchers manually designed architectures to fit their data and objectives, but NAS finds the optimal architecture by leveraging a Search Space (activation functions, number of layers, etc.) and a Search Strategy (reinforcement learning, evolutionary methods, etc.).
The biggest drawback of early NAS was the massive time and cost caused by repeated experiments. To address this, active research is being conducted on stabilizing Gradient-Based NAS and on using a Performance Predictor to predict the performance of an architecture without fully completing training.
At this year's NeurIPS 2021, a total of 15 papers related to NAS were accepted, with particular attention given to research addressing the efficiency of performance predictors. Key approaches include Quantization, Meta Learning, Graph Neural Network, and Ensemble.
This summary was generated automatically by AI. Check the original for the author's claims and context. Copyright belongs to the original author.
Our guide explains how the AI works. Report summary errors, attribution issues, or removal requests via Contact.