The DOI matches its Crossref record; 51 of the 52 references it cites were found in a registry or an index.
Multitask Learning
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Citations
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.
Cited by
Sorted by citations
Multi-task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
Cipolla, R., Gal, Y., & Kendall, A. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00781 · Cited by 3,867
Ranjan, R., Patel, V. M., & Chellappa, R. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2781233 · Cited by 1,333
End-To-End Multi-Task Learning With Attention
Liu, S., Johns, E., & Davison, A. J. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00197 · Cited by 1,299
Norouzzadeh, M. S., Nguyen, A., Kosmala, M., Swanson, A., Palmer, M. S., Packer, C., & Clune, J. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1719367115 · Cited by 1,230
Deep Architecture for Traffic Flow Prediction: Deep Belief Networks With Multitask Learning
Huang, W., Song, G., Hong, H., & Xie, K. (2014) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2014.2311123 · Cited by 1,139
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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