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AutoML: A survey of the state-of-the-art
Xin He
, Kaiyong Zhao
, Xiaowen Chu
*
*
Corresponding author for this work
Department of Computer Science
Research output
:
Contribution to journal
›
Journal article
›
peer-review
1541
Citations (Scopus)
Overview
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Dive into the research topics of 'AutoML: A survey of the state-of-the-art'. Together they form a unique fingerprint.
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Keyphrases
Neural Architecture Search
100%
AutoML
100%
Machine Learning Techniques
28%
Deep Learning System
28%
Hyperparameter Optimization
28%
Search Methods
14%
Search Algorithm
14%
Deep Learning Methods
14%
Resource Base
14%
Image Recognition
14%
Object Detection
14%
Algorithm Performance
14%
Feature Engineering
14%
Promising Solutions
14%
Specific Task
14%
Language Modeling
14%
ImageNet Dataset
14%
Deep Learning Pipeline
14%
Object-oriented Languages
14%
Optimized Architecture
14%
Human Assistance
14%
Up-to-date Review
14%
Preparation Features
14%
Human Expertise
14%
Data Preparation
14%
CIFAR10 Dataset
14%
Architecture Optimization
14%
Computer Science
Automated Machine Learning
100%
Neural Architecture Search
100%
Deep Learning Method
42%
Learning System
28%
Searching Algorithm
14%
Language Modeling
14%
Leaning Parameter
14%
Algorithm Performance
14%
Deep Learning Technique
14%
Object Detection
14%
Data Preparation
14%
Hyperparameter Optimization
14%
Feature Engineering
14%