The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
The MNIST Database of Handwritten Digit Images for Machine Learning Research [Best of the Web]
Abstract
In this issue, “Best of the Web” presents the modified National Institute of Standards and Technology (MNIST) resources, consisting of a collection of handwritten digit images used extensively in optical character recognition and machine learning research.
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Cites
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
What is the best multi-stage architecture for object recognition?
Jarrett, K., Kavukcuoglu, K., Ranzato, M. A., & LeCun, Y. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459469 · Cited by 2,153
(2011) · doi:10.5591/978-1-57735-516-8/ijcai11-210 · Cited by 1,229
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Recent advances in convolutional neural networks
Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2017.10.013 · Cited by 6,576
A survey of the recent architectures of deep convolutional neural networks
Khan, A., Sohail, A., Zahoora, U., & Qureshi, A. S. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-020-09825-6 · Cited by 3,137
Decentralized federated learning: Fundamentals, state of the art, frameworks, trends, and challenges
Martínez Beltrán, E. T., Pérez, M. Q., Sánchez, P. M. S., Bernal, S. L., Bovet, G., Pérez, M. G., Pérez, G. M., & Celdrán, A. H. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3315746 · Cited by 625
“[52] 2021 A method for D2D network in industrial IoT devices for improving the convergence of communications Cross-device Decentralized Real MobileNet FedAvg (consensus) Asynchronous Random MNIST [172]”
BackgroundMethodInfluentialThe Specious Art of Single-Cell Genomics
Chari, T., & Pachter, L. (2021) · doi:10.1101/2021.08.25.457696 · Cited by 379
Towards Visually Explaining Variational Autoencoders
Liu, W., Li, R., Zheng, M., Karanam, S., Wu, Z., Bhanu, B., Radke, R. J., & Camps, O. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00867 · Cited by 294
Metadata
- Authors
- Deng, L.
- Venue
- IEEE Signal Processing Magazine
- Published
- 2012-11
- Volume
- 29
- Issue
- 6
- Pages
- 141-142
- ISSN
- 1053-5888 (ISSN-L)
- OpenAlex
- W2007339694
- Cited by
- 4,836
- References
- 3
- FWCI
- 5.25
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
