Reinforced active learning for CVD-grown two-dimensional materials characterization-Reference-Cited by-同舟云学术

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Reinforced active learning for CVD-grown two-dimensional materials characterization

Published:2023-07-26 Issue: Volume: Page:1-13
ISSN:2472-5854
Container-title:IISE Transactions
language:en
Short-container-title:IISE Transactions

Author:

Li Zebin¹,Yao Fei²,Sun Hongyue¹^ORCID

Affiliation:

1. Department of Industrial and Systems Engineering, University at Buffalo, The State University of New York, Buffalo, NY, USA

2. Department of Materials Design and Innovation, University at Buffalo, The State University of New York, Buffalo, NY, USA

Funder

New York State Energy Research and Development Authority

New York State Center of Excellence in Materials Informatics

Publisher

Informa UK Limited

Subject

Industrial and Manufacturing Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/24725854.2023.2227659

Reference62 articles.

1. Aggarwal, C.C., Hinneburg, A. and Keim, D.A. (2001) On the surprising behavior of distance metrics in high dimensional space. International Conference on Database Theory.

2. Beluch, W.H., Genewein, T., Nürnberger, A. and Köhler, J.M. (2018) The power of ensembles for active learning in image classification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE Press, Piscataway, NJ, pp. 9368–9377.

3. Skill-based curiosity for intrinsically motivated reinforcement learning

4. A survey on active learning and human-in-the-loop deep learning for medical image analysis

5. Machine learning-driven new material discovery

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