Training data augmentation: An empirical study using generative adversarial net-based approach with normalizing flow models for materials informatics-Reference-Cited by-同舟云学术

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Training data augmentation: An empirical study using generative adversarial net-based approach with normalizing flow models for materials informatics

Published:2020-01 Issue: Volume:86 Page:105932
ISSN:1568-4946
Container-title:Applied Soft Computing
language:en
Short-container-title:Applied Soft Computing

Author:

Ohno Hiroshi

Publisher

Elsevier BV

Subject

Software

Reference32 articles.

1. Screening billions of candidates for solid lithium-ion conductors: A transfer learning approach for small data;Cubuk;J. Chem. Phys.,2019

2. A strategy to apply machine learning to small datasets in materials science;Zhang;NPJ Comput. Mater.,2018

3. A statistical learning framework for materials science: Application to elastic moduli of k-nary inorganic polycrystalline compounds;de Jong;Sci. Rep.,2016

4. Machine learning in materials informatics: recent applications and prospects;Ramprasad;NPJ Comput. Mater.,2017

5. Learning deep sigmoid belief networks with data augmentation;Gan,2015

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