The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
The Unreasonable Effectiveness of Data
Abstract
Problems that involve interacting with humans, such as natural language understanding, have not proven to be solvable by concise, neat formulas like F = ma. Instead, the best approach appears to be to embrace the complexity of the domain and address it by harnessing the power of data: if other humans engage in the tasks and generate large amounts of unlabeled, noisy data, new algorithms can be used to build high-quality models from the data.
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Cites
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A Comprehensive Grammar of the English Language
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The Unreasonable Effectiveness of Mathematics in the Natural Sciences
Wigner, E. P. (1990) · Mathematics and Science · doi:10.1142/9789814503488_0018 · Cited by 1,135
WebTables: exploring the power of tables on the web
Cafarella, M. J., Halevy, A., Wang, D. Z., Wu, E., & Zhang, Y. (2008) · Proceedings of the VLDB Endowment · doi:10.14778/1453856.1453916 · Cited by 629
Self-citationThe Unreasonable Effectiveness of Mathematics
Hamming, R. W. (1980) · American Mathematical Monthly · doi:10.1080/00029890.1980.11994966 · Cited by 195
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Hays, J. H., & Efros, A. A. (2008) · Communications of the ACM · doi:10.1145/1400181.1400202 · Cited by 129
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Cited by
Boyd, S., Parikh, N., Chu, E., Peleato, B., & Eckstein, J. (2011) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000016 · Cited by 17,075
A survey on Image Data Augmentation for Deep Learning
Shorten, C., & Khoshgoftaar, T. M. (2019) · Journal of Big Data · doi:10.1186/s40537-019-0197-0 · Cited by 13,203
Rajkomar, A., Dean, J., & Kohane, I. (2019) · New England Journal of Medicine · doi:10.1056/nejmra1814259 · Cited by 4,579
Predicting the future - big data, machine learning, and clinical medicine
Obermeyer, Z., & Emanuel, E. J. (2016) · New England Journal of Medicine · doi:10.1056/nejmp1606181 · Cited by 3,652
The Effectiveness of Data Augmentation in Image Classification using Deep Learning
Perez, L., & Wang, J. (2017) · arXiv · doi:10.48550/arxiv.1712.04621 · Cited by 2,499
Metadata
- Authors
- Halevy, A., Norvig, P., & Pereira, F.
- Venue
- IEEE Intelligent Systems
- Published
- 2009-03
- Volume
- 24
- Issue
- 2
- Pages
- 8-12
- ISSN
- 1541-1672 (ISSN-L)
- OpenAlex
- W2103018059
- Cited by
- 1,767
- References
- 13
- FWCI
- 57.11
- Type
- journal article
- Language
- en
- Open access
- closed
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- Halevy, A., Norvig, P., & Pereira, F. (2009). The Unreasonable Effectiveness of Data. IEEE Intelligent Systems. https://doi.org/10.1109/mis.2009.36
Data sources
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.
