The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
Deep Learning of Representations: Looking Forward
Abstract
Deep learning research aims at discovering learning algorithms that discover multiple levels of distributed representations, with higher levels representing more abstract concepts. Although the study of deep learning has already led to impressive theoretical results, learning algorithms and breakthrough experiments, several challenges lie ahead. This paper proposes to examine some of these challenges, centering on the questions of scaling deep learning algorithms to much larger models and datasets, reducing optimization difficulties due to ill-conditioning or local minima, designing more efficient and powerful inference and sampling procedures, and learning to disentangle the factors of variation underlying the observed data. It also proposes a few forward-looking research directions aimed at overcoming these challenges.
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Cites
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Self-citationLearning representations by back-propagating errors
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Self-citationImproving neural networks by preventing co-adaptation of feature detectors
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“Another option, following the idea of curriculum learning (Bengio et al., 2009), is to provide guidance ourselves to learning machines (as exemplified in the toy example of Gulcehre and Bengio (2013)), by “teaching them” gradually more complex concepts to help them understand the world around us (keeping in mind that…”
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Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review
Rawat, W., & Wang, Z. (2017) · Neural Computation · doi:10.1162/neco_a_00990 · Cited by 3,503
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
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
Deep Metric Learning Using Triplet Network
Hoffer, E., & Ailon, N. (2015) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-24261-3_7 · Cited by 2,134
Deep learning for sensor-based activity recognition: A survey
Wang, J., Chen, Y., Hao, S., Peng, X., & Hu, L. (2019) · Pattern Recognition Letters · doi:10.1016/j.patrec.2018.02.010 · Cited by 2,014
Metadata
- Authors
- Bengio, Y.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2013
- Pages
- 1-37
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W1613249581
- Cited by
- 513
- References
- 25
- FWCI
- 20.38
- Type
- conference paper
- Language
- en
- Open access
- closed
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