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3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol

3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol

Published on: May 12, 2019

Advances in neural architecture search.

Xin Wang¹, Wenwu Zhu¹

¹Department of Computer Science and Technology, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China.

National Science Review

|September 12, 2024

Summary

Neural Architecture Search (NAS) automates machine learning model design. This paper reviews NAS advances, applications, tools, and benchmarks for efficient, adaptable AI development.

Keywords:

artificial intelligence machine learning neural architecture search

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Area of Science:

Artificial Intelligence
Machine Learning
Deep Learning

Background:

Automated Machine Learning (AutoML) automates complex machine learning model design.
Neural Architecture Search (NAS) is a key AutoML area, optimizing neural network structures.
NAS significantly enhances performance across diverse real-world applications.

Purpose of the Study:

To provide a comprehensive overview of Neural Architecture Search (NAS).
To elaborate on recent advances, applications, tools, and benchmarks in NAS.
To discuss prospective research directions in automated neural architecture design.

Main Methods:

Defining appropriate search spaces for neural architectures.
Designing effective search strategies for efficient exploration.
Developing robust evaluation mechanisms for performance assessment.

Main Results:

NAS enables systematic exploration of complex architecture spaces.
Efficiency improvements through techniques like weight sharing and evaluation estimation.
Adaptability of NAS across diverse data types including graphs, tabular data, and videos.

Conclusions:

NAS is a powerful technique for automating and optimizing machine learning model design.
Standardized benchmarks facilitate comparison and advancement of NAS methods.
Future research directions focus on enhancing NAS efficiency and applicability.