The DOI matches its Crossref record; 52 of the 53 references it cites were found in a registry or an index.
In situ training of feed-forward and recurrent convolutional memristor networks
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“Although our device is not ideal yet, the estimated accuracy is comparable to other hardware‐based simulation performances ranging from 70% to 94%.[ 41 , 42 , 43 , 44 , 45 ]”
From Retention Secured Nonlinear and Self-Rectifying Analog Charge Trap Memristor for Energy-Efficient Neuromorphic Hardware (2023) · cites this paper as [41]
Citations
Cites
Deep Residual Learning for Image Recognition
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Self-citationSpeech recognition with deep recurrent neural networks
Graves, A., Mohamed, A.-R., & Hinton, G. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6638947 · Cited by 8,893
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Research works printed but not linked: 2
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- Tieleman, T. & Hinton, G. Lecture 6.5-rmsprop: divide the gradient by a running average of its recent magnitude. COURSERA: Neural networks for machine learning 4, 26–31 (2012).
Cited by
Sorted by citations
Resistive switching materials for information processing
Wang, Z., Wu, H., Burr, G. W., Hwang, C. S., Wang, K. L., Xia, Q., & Yang, J. J. (2020) · Nature Reviews Materials · doi:10.1038/s41578-019-0159-3 · Cited by 1,214
Self-citationTang, J., Yuan, F., Shen, X., Wang, Z., Rao, M., He, Y., Sun, Y., Li, X., Zhang, W., Li, Y., et al. (2019) · Advanced Materials · doi:10.1002/adma.201902761 · Cited by 850
Self-citationDynamical memristors for higher-complexity neuromorphic computing
Kumar, S., Wang, X., Strachan, J. P., Yang, Y., & Lu, W. D. (2022) · Nature Reviews Materials · doi:10.1038/s41578-022-00434-z · Cited by 584
Self-citationThe Future of Memristors: Materials Engineering and Neural Networks
Sun, K., Chen, J., & Yan, X. (2020) · Advanced Functional Materials · doi:10.1002/adfm.202006773 · Cited by 491
In-sensor reservoir computing for language learning via two-dimensional memristors
Sun, L., Wang, Z., Jiang, J., Kim, Y., Joo, B., Zheng, S., Lee, S., Yu, W. J., Kong, B., & Yang, H. (2021) · Science Advances · doi:10.1126/sciadv.abg1455 · Cited by 450
Self-citation
Metadata
- Authors
- Wang, Z., Li, C., Lin, P., Rao, M., Nie, Y., Song, W., Qiu, Q., Li, Y., Yan, P., Strachan, J. P., Ge, N., McDonald, N., Wu, Q., Hu, M., Wu, H., Williams, R. S., Xia, Q., & Yang, J. J.
- Affiliations
- University of Massachusetts Amherst (US)
- Hewlett-Packard (United States) (US)
- Syracuse University (US)
- United States Air Force Research Laboratory (US)
- Binghamton University (US)
- Tsinghua University (CN)
- Texas A&M University (US)
- Corresponding author
- Xia, Qiangfei
- Yang, J. Joshua
- ORCID
- Venue
- Nature Machine Intelligence
- Published
- 2019-09-09
- Volume
- 1
- Issue
- 9
- Pages
- 434-442
- ISSN
- 2522-5839 (ISSN-L)
- OpenAlex
- W2972686555
- Cited by
- 307
- References
- 53
- Citations by year
- 2026: 16 · 2025: 39 · 2024: 46 · 2023: 59 · 2022: 50 · 2021: 59 · 2020: 35 · 2019: 3
- FWCI
- 13.21
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Nature Machine Intelligence (publishedVersion)
- Funders
- National Natural Science Foundation of China (61674092)
- United States Department of Defense | United States Air Force | AFMC | Air Force Research Laboratory (FA8750-18-2-0122)
- United States Department of Defense | Defense Advanced Research Projects Agency (D17PC00304)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Reservoir Computing
- Keywords
- memristor neural networks, in situ training, convolutional neural networks, recurrent neural networks, convolutional long short-term memory, parameter sharing, energy-efficient hardware, edge AI, MNIST classification, parameter reduction
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