The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Learning Deep Generative Models
Open accessAbstract
Building intelligent systems that are capable of extracting high-level representations from high-dimensional sensory data lies at the core of solving many artificial intelligence–related tasks, including object recognition, speech perception, and language understanding. Theoretical and biological arguments strongly suggest that building such systems requires models with deep architectures that involve many layers of nonlinear processing. In this article, we review several popular deep learning models, including deep belief networks and deep Boltzmann machines. We show that (a) these deep generative models, which contain many layers of latent variables and millions of parameters, can be learned efficiently, and (b) the learned high-level feature representations can be successfully applied in many application domains, including visual object recognition, information retrieval, classification, and regression tasks.
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Cites
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Deep learning in medical image analysis
Shen, D., Wu, G., & Suk, H.-I. (2017) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-071516-044442 · Cited by 4,968
Deep Learning in Medical Ultrasound Analysis: A Review
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Kadurin, A., Nikolenko, S., Khrabrov, K., Aliper, A., & Zhavoronkov, A. (2017) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.7b00346 · Cited by 587
Artificial Intelligence and Digital Pathology: Challenges and Opportunities
Tizhoosh, H. R., & Pantanowitz, L. (2018) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_53_18 · Cited by 544
Artificial intelligence in drug discovery: recent advances and future perspectives
Jiménez-Luna, J., Grisoni, F., Weskamp, N., & Schneider, G. (2021) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2021.1909567 · Cited by 464
Metadata
- Authors
- Salakhutdinov, R.
- Venue
- Annual Review of Statistics and Its Application
- Publisher
- Annual Reviews
- Published
- 2015-04-10
- Volume
- 2
- Issue
- 1
- Pages
- 361-385
- ISSN
- 2326-8298 (ISSN-L)
- 2326-831X
- OpenAlex
- W2164700406
- Cited by
- 436
- References
- 16
- FWCI
- 14.17
- Type
- journal article
- Language
- en
- Open access
- bronze
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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.
