The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Neural network studies. 1. Comparison of overfitting and overtraining
Abstract
ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTNeural network studies. 1. Comparison of overfitting and overtrainingIgor V. Tetko, David J. Livingstone, and Alexander I. LuikCite this: J. Chem. Inf. Comput. Sci. 1995, 35, 5, 826–833Publication Date (Print):September 1, 1995Publication History Published online1 May 2002Published inissue 1 September 1995https://pubs.acs.org/doi/10.1021/ci00027a006https://doi.org/10.1021/ci00027a006research-articleACS PublicationsRequest reuse permissionsArticle Views3447Altmetric-Citations500LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail Ot…
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“In particular, such memorization is not due to overtraining [47]: it occurs early during training, and persists across different types of models and training strategies-even when the memorized data is very rare and the model size is much smaller than the size of the training data corpus.”
From The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks (2018) · cites this paper as [47]
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Cites
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Thodberg, H. H. (1996) · IEEE Transactions on Neural Networks · doi:10.1109/72.478392 · Cited by 184
Statistics using neural networks: chance effects
Livingstone, D. J., & Manallack, D. T. (1993) · Journal of Medicinal Chemistry · doi:10.1021/jm00061a023 · Cited by 105
Self-citationApplications of neural networks in structure-activity relationships of a small number of molecules
Tetko, I. V., Luik, A. I., & Poda, G. I. (1993) · Journal of Medicinal Chemistry · doi:10.1021/jm00059a003 · Cited by 86
Self-citationSimon, V., Gasteiger, J., & Zupan, J. (1993) · Journal of the American Chemical Society · doi:10.1021/ja00073a034 · Cited by 85
Analysis of linear and nonlinear QSAR data using neural networks
Manallack, D. T., Ellis, D. D., & Livingstone, D. J. (1994) · Journal of Medicinal Chemistry · doi:10.1021/jm00048a012 · Cited by 85
Self-citationThe use of neural networks for variable selection in QSAR
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Comparison of optimized backpropagation algorithms.
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Optimal linear combinations of neural networks: an overview
Hashem, S. R., Schmeiser, B. W., & Yih, Y. (2002) · IEEE International Conference on Neural Networks/IEEE ... International Conference on Neural Networks · doi:10.1109/icnn.1994.374511 · Cited by 72
Regression analysis for QSAR using neural networks
Livingstone, D. J., & Salt, D. W. (1992) · Bioorganic & Medicinal Chemistry Letters · doi:10.1016/s0960-894x(01)81067-2 · Cited by 34
Self-citationSimple heuristic methods for input parameters' estimation in neural networks
Tetko, I. V., Tanchuk, V. Y., & Luik, A. I. (1994) · doi:10.1109/icnn.1994.374193 · Cited by 8
Self-citation
Cited by
Introduction to multi-layer feed-forward neural networks
Svozil, D., Kvasnicka, V., & Pospichal, J. (1997) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/s0169-7439(97)00061-0 · Cited by 1,600
Deep Learning in Drug Discovery
Gawehn, E., Hiss, J. A., & Schneider, G. (2016) · Molecular Informatics · doi:10.1002/minf.201501008 · Cited by 723
The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks
Carlini, N., Liu, C., Erlingsson, Ú., Kos, J., & Song, D. (2018) · arXiv · doi:10.48550/arxiv.1802.08232 · Cited by 629
Neural networks in multivariate calibration
Despagne, F., & Luc Massart, D. (1998) · The Analyst · doi:10.1039/a805562i · Cited by 462
Big data and artificial intelligence modeling for drug discovery
Zhu, H. (2020) · Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-010919-023324 · Cited by 455
Metadata
- Authors
- Tetko, I. V., Livingstone, D. J., & Luik, A. I.
- Venue
- Journal of Chemical Information and Computer Sciences
- Publisher
- American Chemical Society (ACS)
- Published
- 1995-09-01
- Volume
- 35
- Issue
- 5
- Pages
- 826-833
- ISSN
- 0095-2338
- 1520-5142
- Cited by
- 574
- References
- 19
- Language
- en
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