The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Multitask Learning: A Knowledge-Based Source of Inductive Bias
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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
Cited by
Caruana, R. (1997) · Machine Learning · doi:10.1023/a:1007379606734 · Cited by 6,636
Self-citationRegion-based convolutional networks for accurate object detection and segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2437384 · Cited by 2,912
How to Fine-Tune BERT for Text Classification?
Sun, C., Qiu, X., Xu, Y., & Huang, X. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-32381-3_16 · Cited by 1,534
Domain Adaptation for Statistical Classifiers
Daume III, H., & Marcu, D. (2006) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1872 · Cited by 816
Caruana, R. (1998) · Learning to Learn · doi:10.1007/978-1-4615-5529-2_5 · Cited by 577
Self-citationLearning to Learn: Introduction and Overview
Thrun, S., & Pratt, L. (1998) · Learning to Learn · doi:10.1007/978-1-4615-5529-2_1 · Cited by 521
(1995) · Cited by 520
CHILD: A First Step Towards Continual Learning
Ring, M. B. (1997) · Machine Learning · doi:10.1023/a:1007331723572 · Cited by 143
Efficiently Identifying Task Groupings for Multi-Task Learning
Fifty, C., Amid, E., Zhao, Z., Yu, T., Anil, R., & Finn, C. (2021) · arXiv · doi:10.48550/arxiv.2109.04617 · Cited by 125
Varnek, A., Gaudin, C., Marcou, G., Baskin, I., Pandey, A. K., & Tetko, I. V. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci8002914 · Cited by 86
Child: A First Step Towards Continual Learning
Ring, M. B. (1998) · Learning to Learn · doi:10.1007/978-1-4615-5529-2_11 · Cited by 84
Multitask Sparse Bayesian Learning with Applications in Structural Health Monitoring
Huang, Y., Beck, J. L., & Li, H. (2019) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12408 · Cited by 79
Environment-Agnostic Multitask Learning for Natural Language Grounded Navigation
Wang, X. E., Jain, V., Ie, E., Wang, W. Y., Kozareva, Z., & Ravi, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58586-0_25 · Cited by 54
Snorkel MeTaL: Weak Supervision for Multi-Task Learning
Ratner, A., Hancock, B., Dunnmon, J., Goldman, R., & Ré, C. (2018) · Proceedings of the Second Workshop on Data Management for End-To-End Machine Learning · doi:10.1145/3209889.3209898 · Cited by 43
Losses over Labels: Weakly Supervised Learning via Direct Loss Construction
Sam, D., & Kolter, J. Z. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i8.26159 · Cited by 6
Ahmed, F., & Courville, A. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.5712 · Cited by 5
SciRepEval: A Multi-Format Benchmark for Scientific Document Representations
Singh, A., D'Arcy, M., Cohan, A., Downey, D., & Feldman, S. (2022) · arXiv · doi:10.48550/arxiv.2211.13308 · Cited by 76
“Multi-Task Learning Across Formats Multi-task learning (Caruana, 1993) with deep neural networks has shown improvements upon single-task training for related objectives (Liu et al., 2015, 2019b).”
Metadata
- Authors
- Caruana, R. A.
- Venue
- Machine Learning Proceedings 1993
- Publisher
- Elsevier
- Published
- 1993
- Pages
- 41-48
- OpenAlex
- W1614862348
- Cited by
- 679
- References
- 2
- FWCI
- 6.00
- Type
- book chapter
- Language
- en
- Open access
- closed
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
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.
