The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
Material structure-property linkages using three-dimensional convolutional neural networks
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Cites
Deep Residual Learning for Image Recognition
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ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Gradient-based learning applied to document recognition
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Book Review: Pattern Recognition and Machine Learning
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Convolutional Neural Networks for Sentence Classification
Kim, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1181 · Cited by 14,831
Advances in Neural Information Processing Systems 25
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Convolutional Neural Networks for Speech Recognition
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Cited by
Recent advances and applications of deep learning methods in materials science
Choudhary, K., DeCost, B., Chen, C., Jain, A., Tavazza, F., Cohn, R., Park, C. W., Choudhary, A., Agrawal, A., Billinge, S. J. L., et al. (2022) · npj Computational Materials · doi:10.1038/s41524-022-00734-6 · Cited by 1,172
“, predicting the macroscale property of a material given its microstructure information [259, 261, 285], as well as for localization, i.”
BackgroundMachine learning in materials science
Wei, J., Chu, X., Sun, X., Xu, K., Deng, H., Chen, J., Wei, Z., & Lei, M. (2019) · InfoMat · doi:10.1002/inf2.12028 · Cited by 1,069
Using deep neural network with small dataset to predict material defects
Feng, S., Zhou, H., & Dong, H. (2019) · Materials & Design · doi:10.1016/j.matdes.2018.11.060 · Cited by 512
Emerging materials intelligence ecosystems propelled by machine learning
Batra, R., Song, L., & Ramprasad, R. (2020) · Nature Reviews Materials · doi:10.1038/s41578-020-00255-y · Cited by 446
Self-citationBock, F. E., Aydin, R. C., Cyron, C. J., Huber, N., Kalidindi, S. R., & Klusemann, B. (2019) · Frontiers in Materials · doi:10.3389/fmats.2019.00110 · Cited by 403
Self-citation
Metadata
- Authors
- Cecen, A., Dai, H., Yabansu, Y. C., Kalidindi, S. R., & Song, L.
- Venue
- Acta Materialia
- Publisher
- Elsevier BV
- Published
- 2018-03
- Volume
- 146
- Pages
- 76-84
- ISSN
- 1359-6454 (ISSN-L)
- OpenAlex
- W2777965033
- Cited by
- 336
- References
- 36
- FWCI
- 7.79
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Cecen, A., Dai, H., Yabansu, Y. C., Kalidindi, S. R., & Song, L. (2018). Material structure-property linkages using three-dimensional convolutional neural networks. Acta Materialia. https://doi.org/10.1016/j.actamat.2017.11.053
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