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“Multi-task (MT) learning 8 was proposed partially to deal with data sparsity problem, which is commonly encountered in QSAR applications.”
From Quantitative Toxicity Prediction Using Topology Based Multitask Deep Neural Networks (2018) · cites this paper as [8]
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Cites
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892
Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439
Backpropagation Applied to Handwritten Zip Code Recognition
LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.4.541 · Cited by 11,891
Proceedings of the 24th international conference on Machine learning
(2007) · doi:10.1145/1273496 · Cited by 11,869
Caruana, R. (1997) · Machine Learning · doi:10.1023/a:1007379606734 · Cited by 6,636
Self-citationStatistical Analysis With Missing Data
Ghosh, S., Little, R. J. A., & Rubin, D. B. (1988) · Technometrics · doi:10.2307/1269814 · Cited by 4,730
Causation, Prediction, and Search
Spirtes, P., Glymour, C., & Scheines, R. (1993) · Lecture Notes in Statistics · doi:10.1007/978-1-4612-2748-9 · Cited by 4,617
A Bayesian Method for the Induction of Probabilistic Networks from Data
Cooper, G. F., & Herskovits, E. H. (1992) · Machine Learning · doi:10.1023/a:1022649401552 · Cited by 4,241
Hierarchical Mixtures of Experts and the EM Algorithm
Jordan, M. I., & Jacobs, R. A. (1994) · Neural Computation · doi:10.1162/neco.1994.6.2.181 · Cited by 2,786
Proceedings of the International Joint Conference on Artificial Intelligence 2007
Bundy, A. (2007) · International Joint Conference on Artificial Intelligence · Cited by 2,761
Research works printed but not linked: 7
- Baxter, J., “Learning Internal Representations,” Ph.D. Thesis, The Flinders Univeristy of South Australia, Dec. 1994.
- Caruana, R., “Multitask Connectionist Learning,” Proceedings of the 1993 Connectionist Models Summer School, 1994, pp. 372–379.
- Caruana, R., Baluja, S., and Mitchell, T, “Using the Future to “Sort Out” the Present: Rankprop and Multitask Learning for Medical Risk Prediction,” Advances in Neural Information Processing Systems 8, (Proceedings of NIPS-95), 1996, pp. 959–965.
- de Sa, V. R., “Learning Classification with Unlabelled Data,” Advances in Neural Information Processing Systems 6, (Proceedings of NIPS-93), 1994, pp. 112–119.
- Martin, J. D., “Goal-directed Clustering,” Proceedings of the 1994 AAAI Spring Symposium on Goal-directed Learning, 1994.
- O’Sullivan, J. and Thrun, S., “Discovering Structure in Multiple Learning Tasks: The TC Algorithm,” Proceedings of the 13th International Conference on Machine Learning, ICML-96, Bari, Italy, 1996, pp. 489–497.
- Simard, P., Victorri, B., LeCun, Y, and Denker, J., “Tangent Prop—A Formalism for Specifying Selected Invariances in an Adaptive Neural Network,” Advances in Neural Information Processing Systems 4, (Proceedings of NIPS-91) 1992, pp. 895–903.
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Metadata
- Authors
- Caruana, R.
- Corresponding author
- Caruana, Rich
- ORCID
- Caruana, Rich
- Venue
- Learning to Learn
- Publisher
- Springer US
- Published
- 1998
- Pages
- 95-133
- OpenAlex
- W4239943352
- Cited by
- 577
- References
- 52
- Citations by year
- 2026: 6 · 2025: 20 · 2024: 39 · 2023: 106 · 2022: 98 · 2021: 31 · 2020: 27 · 2019: 52 · 2018: 99 · 2017: 63 · 2016: 9 · 2015: 14 · 2014: 4 · 2013: 1 · 2012: 1
- FWCI
- 0.00
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Springer eBooks (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Keywords
- multi-task learning, inductive transfer, inductive bias, shared representations, task relatedness, k-nearest neighbors, kernel regression, decision tree, generalization
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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