Automated Machine Learning
Automated Machine Learning is a predictive modeling framework that distinguishes itself by systematically integrating and optimizing machine learning pipeline components—such as feature preprocessing, model selection, hyperparameter tuning, and validation—through algorithmic search and evaluation, thereby minimizing manual intervention in the model development process.
1.8K
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104.8K
Citations
7.4K
Authors
2.3K
Institutions
Publications per year
2017–2026
1.5K
Parents
1.8K
Deep Learning Approach for Intelligent Intrusion Detection System
R. Vinayakumar, Mamoun Alazab, K. P. Soman et al. · IEEE Access · 2019 · 1.8K citations · Full text
James Bergstra, Daniel Yamins, David Cox · Digital Access to Scholarship at Harvard (DASH) (Harvard University) · 2013 · 1.6K citations · Full text
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University College London
London, United Kingdom
20 h-index
68 publications
7.8K citations
University of California, Berkeley
Berkeley, United States
17 h-index
39 publications
6.2K citations
Carnegie Mellon University
Pittsburgh, United States
16 h-index
53 publications
5K citations
Stanford University
Stanford, United States
16 h-index
34 publications
3K citations
Columbia University
New York, United States
16 h-index
35 publications
1.9K citations
Massachusetts Institute of Technology
Cambridge, United States
15 h-index
41 publications
8K citations
University of Washington
Seattle, United States
15 h-index
39 publications
8.5K citations
Harvard University
Cambridge, United States
15 h-index
43 publications
26K citations
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Repository · 84 publications · 3.8K citations
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28 publications · 3.7K citations
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