The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Deep Machine Learning - A New Frontier in Artificial Intelligence Research [Research Frontier]
Abstract
This article provides an overview of the mainstream deep learning approaches and research directions proposed over the past decade. It is important to emphasize that each approach has strengths and "weaknesses, depending on the application and context in "which it is being used. Thus, this article presents a summary on the current state of the deep machine learning field and some perspective into how it may evolve. Convolutional Neural Networks (CNNs) and Deep Belief Networks (DBNs) (and their respective variations) are focused on primarily because they are well established in the deep learning field and show great promise for future work.
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Cites
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A fast learning algorithm for deep belief nets
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Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex
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“To obtain generative weights, the initial pretraining occurs in an unsupervised greedy layer-by-layer manner, enabled by what Hinton has termed contrastive divergence [15].”
MethodGreedy layer-wise training of deep networks
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Cited by
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570
Deep learning for computer vision: A brief review
Voulodimos, A., Doulamis, N., Doulamis, A., & Protopapadakis, E. (2018) · Computational Intelligence and Neuroscience · doi:10.1155/2018/7068349 · Cited by 3,403
A survey of deep neural network architectures and their applications
Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., & Alsaadi, F. E. (2017) · Neurocomputing · doi:10.1016/j.neucom.2016.12.038 · Cited by 3,239
Data-intensive applications, challenges, techniques and technologies: A survey on Big Data
Philip Chen, C. L., & Zhang, C.-Y. (2014) · Information Sciences · doi:10.1016/j.ins.2014.01.015 · Cited by 3,047
Deep learning applications and challenges in big data analytics
Najafabadi, M. M., Villanustre, F., Khoshgoftaar, T. M., Seliya, N., Wald, R., & Muharemagic, E. (2015) · Journal of Big Data · doi:10.1186/s40537-014-0007-7 · Cited by 2,594
Metadata
- Authors
- Arel, I., Rose, D. C., & Karnowski, T. P.
- Venue
- IEEE Computational Intelligence Magazine
- Published
- 2010-11
- Volume
- 5
- Issue
- 4
- Pages
- 13-18
- ISSN
- 1556-603X (ISSN-L)
- OpenAlex
- W1998399571
- Cited by
- 1,121
- References
- 11
- FWCI
- 16.95
- Type
- journal article
- Language
- en
- Open access
- closed
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- Arel, I., Rose, D. C., & Karnowski, T. P. (2010). Deep Machine Learning - A New Frontier in Artificial Intelligence Research [Research Frontier]. IEEE Computational Intelligence Magazine. https://doi.org/10.1109/mci.2010.938364
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
