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
Solving Multiclass Learning Problems via Error-Correcting Output Codes
Dietterich, T. G., & Bakiri, G. (1995) · Journal of Artificial Intelligence Research · doi:10.1613/jair.105 · Cited by 2,739
Advances in Neural Information Processing Systems 7
Tesauro, G., Touretzky, D. S., & Leen, T. K. (1995) · Cited by 869
Learning distributed representations of concepts.
Hinton, G. E. (1989) · eScholarship (California Digital Library) · Cited by 827
Multitask Learning: A Knowledge-Based Source of Inductive Bias
Caruana, R. (1993) · Elsevier eBooks · doi:10.1016/b978-1-55860-307-3.50012-5 · Cited by 753
Self-citationNETtalk: a parallel network that learns to read aloud
Sejnowski, T. J., & Rosenberg, C. (1988) · Cited by 671
Predicting Multivariate Responses in Multiple Linear Regression
Breiman, L., & Friedman, J. H. (1997) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/1467-9868.00054 · Cited by 538
(1995) · Cited by 520
Neural Network Perception for Mobile Robot Guidance
Pomerleau, D. (1993) · Springer eBooks · doi:10.1007/978-1-4615-3192-0 · Cited by 481
Experience with a learning personal assistant
Mitchell, T. M., Caruana, R., Freitag, D., McDermott, J. P., & Zabowski, D. (1994) · Communications of the ACM · doi:10.1145/176789.176798 · Cited by 413
Self-citationUsing additive noise in back-propagation training
Holmström, L., & Koistinen, P. (1992) · IEEE Transactions on Neural Networks · doi:10.1109/72.105415 · Cited by 355
Using Sampling and Queries to Extract Rules from Trained Neural Networks
Craven, M. W., & SHAVLIK, J. W. (1994) · Elsevier eBooks · doi:10.1016/b978-1-55860-335-6.50013-1 · Cited by 312
Learning from Incomplete Data.
Zoubin Ghahramani, A., & Jordan, M. I. (1994) · doi:10.21236/ada295618 · Cited by 243
Learning from hints in neural networks
Abu‐Mostafa, Y. S. (1990) · Journal of Complexity · doi:10.1016/0885-064x(90)90006-y · Cited by 239
Learning Many Related Tasks at the Same Time with Backpropagation
Caruana, R. (1994) · Cited by 231
Self-citationAn evaluation of machine-learning methods for predicting pneumonia mortality
Cooper, G. F., Aliferis, C., Ambrosino, R., Aronis, J. M., Buchanan, B. G., Caruana, R. A., Fine, M. J., Glymour, C., Gordon, G. J., Hanusa, B. H., et al. (1997) · Artificial Intelligence in Medicine · doi:10.1016/s0933-3657(96)00367-3 · Cited by 215
Self-citationLearning Internal Representations
Baxter, J. (1995) · Cited by 211
Validation of a pneumonia prognostic index using the MedisGroups comparative hospital database
Fine, M. J., Singer, D. E., Hanusa, B. H., Lave, J. R., & Kapoor, W. N. (1993) · The American Journal of Medicine · doi:10.1016/0002-9343(93)90177-q · Cited by 168
Modularity and scaling in large phonemic neural networks
Waibel, A. H., Sawai, H., & Shikano, K. (1989) · IEEE Transactions on Acoustics Speech and Signal Processing · doi:10.1109/29.45535 · Cited by 156
A personal learning apprentice
Dent, L., Boticario, J. G., McDermott, J. P., Mitchell, T. M., & Zabowski, D. (1992) · Cited by 156
Direct transfer of learned information among neural networks
Pratt, L., Mostow, J., & Kamm, C. (1991) · Cited by 148
Thrun, S., & Mitchell, T. M. (1994) · doi:10.21236/ada285342 · Cited by 141
Abu‐Mostafa, Y. S. (1995) · Neural Computation · doi:10.1162/neco.1995.7.4.639 · Cited by 121
Explanation-Based Neural Network Learning
Thrun, S. (1996) · Kluwer international series in engineering and computer science · doi:10.1007/978-1-4613-1381-6 · Cited by 103
Training Neural Networks with Deficient Data
Tresp, V., Ahmad, S., & Neuneier, R. (1993) · Cited by 92
Abu‐Mostafa, Y. S. (1993) · Neural Computation · doi:10.1162/neco.1993.5.2.278 · Cited by 83
A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping
Dietterich, T. G., Hild, H., & Bakiri, G. (1990) · Elsevier eBooks · doi:10.1016/b978-1-55860-141-3.50007-9 · Cited by 79
Multi-Task Learning for Stock Selection
Ghosn, J., & Bengio, Y. (1996) · Cited by 76
Rule-injection hints as a means of improving network performance and learning time
Suddarth, S. C., & Kergosien, Y. L. (1990) · Lecture notes in computer science · doi:10.1007/3-540-52255-7_33 · Cited by 68
Sill, J., & Abu‐Mostafa, Y. S. (1996) · neural information processing systems · Cited by 67
Symbolic-neural systems and the use of hints for developing complex systems
Suddarth, S. C., & HOLDEN, A. D. C. (1991) · International Journal of Man-Machine Studies · doi:10.1016/s0020-7373(05)80130-0 · Cited by 64
Lifelong Learning: A Case Study.
Thrun, S. (1995) · doi:10.21236/ada303191 · Cited by 49
Conceptual Clustering, Learning from Examples, and Inference
Fisher, D. (1987) · Elsevier eBooks · doi:10.1016/b978-0-934613-41-5.50008-8 · Cited by 48
Promoting Poor Features to Supervisors: Some Inputs Work Better as Outputs
Caruana, R., de Sa, V. R., & de Sa, V. R. (1997) · Cited by 36
Self-citationCompetition among networks improves committee performance
Munro, P., & Parmanto, B. (1997) · Cited by 35
A comparison of ID3 and backpropagation for English text-to-speech mapping
Dietterich, T. G., Hild, H., & Bakiri, G. (1995) · Machine Learning · doi:10.1007/bf00993821 · Cited by 33
Acquiring and Combining Overlapping Concepts
Martin, J., & Billman, D. O. (1994) · Machine Learning · doi:10.1007/bf00993176 · Cited by 30
Mixture Models for Learning from Incomplete Data
(1997) · The MIT Press eBooks · doi:10.7551/mitpress/2016.003.0010 · Cited by 28
A Bayesian/information theoretic model of bias learning
Baxter, J. (1996) · Annual Conference on Computational Learning Theory (COLT) · doi:10.1145/238061.238071 · Cited by 17
Sensor Fusion for Autonomous Outdoor Navigation using Neural Networks
Davis, I. L. (1995) · doi:10.21236/ada293563 · Cited by 17
Omohundro, S. M. (1995) · Cited by 16
A Powerful Heuristic for the Discovery of Complex Patterned Behavior
Valdés‐Pérez, R. E., & Pérez, A. (1994) · Elsevier eBooks · doi:10.1016/b978-1-55860-335-6.50047-7 · Cited by 7
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
