Multitask Learning
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There are two main goals in continual learning: maintaining stability and maintaining plasticity.
“There are two main goals in continual learning: maintaining stability and maintaining plasticity88–91.”
From Loss of plasticity in deep continual learning (2024) · cites this paper as [88]
Section 4.6, “Sequential Transfer” (page unavailable), describes avoiding the forgetting of old tasks while learning new ones, which speaks to both concerns. It does not state that maintaining stability and plasticity are the two main goals of continual learning. (LLM, conf=0.71)
Experimental — accuracy not yet measuredPretraining followed by task-specific fine-tuning can improve generalization.
“pretraining followed by task-specific fine-tuning can improve generalization (Caruana, 1997)”
From Masked Autoencoder Pretraining on Strong-Lensing Images for Joint Dark-Matter Model Classification and Super-Resolution (2025) · cites this paper as Caruana, 1997
Section 1.4 (page unavailable) says inductive transfer can improve generalization, and Section 6 reports improved generalization from a serial transfer method. But the paper’s own method learns related tasks in parallel; it does not establish that pretraining followed by task-specific fine-tuning improves generalization. (LLM, conf=0.82)
Experimental — accuracy not yet measuredA common representation of lensing data can benefit various tasks.
“These studies hint that a common representation of lensing data can benefit various tasks”
From Masked Autoencoder Pretraining on Strong-Lensing Images for Joint Dark-Matter Model Classification and Super-Resolution (2025) · cites this paper as Caruana, 1997
Section 3.2.1 explains that related tasks can benefit from learning a shared feature, and Section 3.2.4 describes a preference for shared hidden-layer representations. The paper does not study lensing data, so it supports the general multitask-learning idea but not its lensing-specific application. Page numbers are unavailable in our copy. (LLM, conf=0.86)
Experimental — accuracy not yet measuredMulti-task learning of related tasks could improve generalization capability by suppressing the shared encoder from learning task-specific features.
“The motivation for the proposed architecture is that the multi-task learning (Caruana, 1997) of related tasks could improve the generalization capability by suppressing the shared encoder from learning any taskspecific features.”
From Robust multi-task learning and online refinement for spacecraft pose estimation across domain gap (2024) · cites this paper as Caruana, 1997
The Abstract says multitask learning can improve generalization by learning related tasks with a shared representation, and §3.2.4 says it biases learning toward representations useful to multiple tasks. But the paper does not say the shared hidden layer is prevented from learning any task-specific features; its Additional information says the networks in those experiments were not forced to share hidden-layer representations. (LLM, conf=0.87)
Experimental — accuracy not yet measuredInspired by Caruana, they employed shared layers to extract hidden features of different battery states and task-specific layers to achieve accurate multi-state monitoring.
“Inspired by Caruana, they employed shared layers to extract hidden features of different battery states and task-specific layers to achieve accurate multi-state monitoring[110].”
From Advancements in the estimation of the state of charge of lithium-ion battery: a comprehensive review of traditional and deep learning approaches (2025) · cites this paper as [110]
ILP naturally supports lifelong, multi-task, and transfer learning, which are considered essential for human-like AI.
“For this reason, ILP naturally supports lifelong (Silver et al., 2013), multi-task (Caruana, 1997), and transfer learning (Torrey & Shavlik, 2009), which are considered essential for human-like AI (Lake et al., 2016).”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Caruana, 1997
Vision Transformers are commonly evaluated using the sliding window procedure from MMSegmentation.
“Vision Transformers are commonly evaluated using the sliding window procedure from MMSegmentation [59] (e.g., [10, 101, 103]).”
From MultiMAE: Multi-modal Multi-task Masked Autoencoders (2022) · cites this paper as [10]
Section 2.2 (1D-DOORS) discusses multitask learning on robot-camera images, not Vision Transformers or their evaluation. Searches for MMSegmentation, sliding-window evaluation, and Vision Transformers found no passage addressing the cited claim. (LLM, conf=0.78)
Experimental — accuracy not yet measuredThe multi-task supervised contrastive framework provides a unified optimization process in which classification and contrastive objectives jointly guide the encoder to capture activity semantics while suppressing user-specific variations.
“Within this formulation, our multi-task supervised contrastive framework provides a unified optimization process in which the classification and contrastive objectives jointly guide the encoder to capture activity semantics while suppressing user-specific variations [37].”
From Towards User-Generalizable Wearable-Sensor-Based Human Activity Recognition: A Multi-Task Contrastive Learning Approach (2025) · cites this paper as [37]
Using multi-task learning to train models to perform a variety of tasks rather than one narrowly defined task makes them more generally useful and often more robust.
“In addition, using multi-task learning 147 to train models to perform a variety of tasks rather than one narrowly defined task, such as multi-cancer detection from histopathology images 148 , makes them more generally useful and often more robust.”
From Deep learning-enabled medical computer vision (2021) · cites this paper as [147]
Multi-Task Learning uses the information in training data as an inductive bias to perform multiple tasks in parallel, and the learning of one task improves the understanding of others through shared representations.
“To increase the robustness Multi-Task Learning (MTL) uses the information in training data as an inductive bias to perform multiple tasks in parallel, and the learning of one task improves the understanding of others through shared representations [82].”
From A mini-review on perturbation modelling across single-cell omic modalities (2024) · cites this paper as [82]
Hard parameter sharing extracts a generic representation for different tasks using the same parameters and is usually applied to avoid overfitting.
“Hard parameter sharing [110]: It consists of extracting a generic representation for different tasks using the same parameters. It is usually applied to avoid overfitting problems.”
From A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets (2022) · cites this paper as [110]
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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 · see citing works
(1992) · Cited by 23,431 · see citing works
Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439 · see citing works
Statistical Analysis With Missing Data
Lahiff, M., Little, R. J. A., & Rubin, D. B. (1989) · Journal of the American Statistical Association · doi:10.2307/2289883 · Cited by 17,384 · see citing works
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 · see citing works
Statistical Analysis With Missing Data
Ghosh, S., Little, R. J. A., & Rubin, D. B. (1988) · Technometrics · doi:10.2307/1269814 · Cited by 4,730 · see citing works
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 · see citing works
Causation, Prediction, and Search
Spirtes, P., Glymour, C., & Scheines, R. (2001) · doi:10.7551/mitpress/1754.001.0001 · Cited by 4,521 · see citing works
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 4,222 · see citing works
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 2,730 · see citing works
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 2,698 · see citing works
Toward optimal feature selection
Koller, D., & Sahami, M. (1996) · Cited by 1,436 · see citing works
Learning distributed representations of concepts.
Hinton, G. E. (1989) · eScholarship (California Digital Library) · Cited by 815 · see citing works
Learning Representations by Back-Propagating Errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (2002) · The MIT Press eBooks · doi:10.7551/mitpress/1888.003.0013 · Cited by 794 · see citing works
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 746 · see citing works
Self-citationNETtalk: a parallel network that learns to read aloud
Sejnowski, T. J., & Rosenberg, C. (1988) · Cited by 671 · see citing works
Supervised learning from incomplete data via an EM approach
Zoubin Ghahramani, A., & Jordan, M. I. (1993) · Cited by 552 · see citing works
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 · see citing works
The Need for Biases in Learning Generalizations
Mitchell, T. M. (2007) · Cited by 520 · see citing works
(1995) · Cited by 520 · see citing works
Neural Network Perception for Mobile Robot Guidance
Pomerleau, D. (1993) · Springer eBooks · doi:10.1007/978-1-4615-3192-0 · Cited by 482 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Learning from Incomplete Data.
Zoubin Ghahramani, A., & Jordan, M. I. (1994) · doi:10.21236/ada295618 · Cited by 243 · see citing works
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 · see citing works
Learning Many Related Tasks at the Same Time with Backpropagation
Caruana, R. (1994) · Cited by 231 · see citing works
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 211 · see citing works
Self-citationTangent Prop - A formalism for specifying selected invariances in an adaptive network
Simard, P., Victorri, B., LeCun, Y., & Denker, J. (1991) · neural information processing systems · Cited by 210 · see citing works
Learning Internal Representations
Baxter, J. (1995) · Cited by 206 · see citing works
Discovering Structure in Multiple Learning Tasks: The TC Algorithm.
Thrun, S., & O’Sullivan, J. A. (1996) · Cited by 201 · see citing works
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 · see citing works
Hierarchical mixtures of experts and the EM algorithm
Jordan, M. I., & Jacobs, R. A. (2005) · IEEE International Conference on Neural Networks/IEEE ... International Conference on Neural Networks · doi:10.1109/ijcnn.1993.716791 · Cited by 162 · see citing works
A personal learning apprentice
Dent, L., Boticario, J. G., McDermott, J. P., Mitchell, T. M., & Zabowski, D. (1992) · Cited by 156 · see citing works
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 155 · see citing works
Explanation-Based Neural Network Learning: A Lifelong Learning Approach
Thrun, S. (2012) · Cited by 153 · see citing works
Direct transfer of learned information among neural networks
Pratt, L., Mostow, J., & Kamm, C. (1991) · Cited by 148 · see citing works
Thrun, S., & Mitchell, T. M. (1994) · doi:10.21236/ada285342 · Cited by 141 · see citing works
Caruana, R., Baluja, S., & Mitchell, T. M. (1995) · neural information processing systems · Cited by 121 · see citing works
Self-citationAbu‐Mostafa, Y. S. (1995) · Neural Computation · doi:10.1162/neco.1995.7.4.639 · Cited by 121 · see citing works
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 · see citing works
Training Neural Networks with Deficient Data
Tresp, V., Ahmad, S., & Neuneier, R. (1993) · Cited by 92 · see citing works
Abu‐Mostafa, Y. S. (1993) · Neural Computation · doi:10.1162/neco.1993.5.2.278 · Cited by 83 · see citing works
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 · see citing works
Multi-Task Learning for Stock Selection
Ghosn, J., & Bengio, Y. (1996) · Cited by 76 · see citing works
Learning internal representations
Baxter, J. (1995) · Annual Conference on Computational Learning Theory (COLT) · doi:10.1145/225298.225336 · Cited by 69 · see citing works
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 · see citing works
Sill, J., & Abu‐Mostafa, Y. S. (1996) · neural information processing systems · Cited by 67 · see citing works
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 66 · see citing works
A Comparison of ID3 and Backpropagation for English Text-To-Speech Mapping
Dietterich, T. G., Hild, H., & Bakiri, G. (1995) · Machine Learning · doi:10.1023/a:1022822623726 · Cited by 58 · see citing works
Lifelong Learning: A Case Study.
Thrun, S. (1995) · doi:10.21236/ada303191 · Cited by 49 · see citing works
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 · see citing works
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 · see citing works
Self-citationCompetition among networks improves committee performance
Munro, P., & Parmanto, B. (1997) · Cited by 35 · see citing works
Acquiring and Combining Overlapping Concepts
Martin, J., & Billman, D. O. (1994) · Machine Learning · doi:10.1007/bf00993176 · Cited by 30 · see citing works
Mixture Models for Learning from Incomplete Data
(1997) · The MIT Press eBooks · doi:10.7551/mitpress/2016.003.0010 · Cited by 28 · see citing works
Sensor fusion for autonomous outdoor navigation using neural networks
Davis, I. L., & Stentz, A. (2002) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.1995.525906 · Cited by 23 · see citing works
Using the Representation in a Neural Network's Hidden Layer for Task-Specific Focus of Attention.
Baluga, S., & Pomerleau, D. (1995) · doi:10.21236/ada296386 · Cited by 18 · see citing works
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 · see citing works
Sensor Fusion for Autonomous Outdoor Navigation using Neural Networks
Davis, I. L. (1995) · doi:10.21236/ada293563 · Cited by 17 · see citing works
Omohundro, S. M. (1995) · Cited by 16 · see citing works
Validation of a pneumonia prognostic index using the MedisGroups Comparative Hospital Database
Fine MJ, S. D. (1994) · American Journal of Infection Control · doi:10.1016/0196-6553(94)90025-6 · Cited by 14 · see citing works
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 · see citing works
Acquiring and Combining Overlapping Concepts
Martin, J., & Billman, D. O. (1994) · Machine Learning · doi:10.1023/a:1022635003060 · Cited by 1 · see citing works
Flagged first, then by citations
3 of the 67 references couldn’t be matched to a known paper.
Research works printed but not linked: 6
- Abu-Mostafa, Y. S. (1995). "Hints," Neural Computation, 7, pp. 639-671.
- Caruana, R. (1994)."Multitask Connectionist Learning," Proceedings of the 1993 Connectionist Models Summer School, pp. 372-379.
- Caruana, R. (1997). "Multitask Learning," Ph.D. Thesis, School of Computer Science, Carnegie Mellon University.
- de Sa, V. R. (1994). "Learning Classification with Unlabelled Data," Advances in Neural Information Processing Systems 6, (Proceedings of NIPS-93), pp. 112-119.
- Liu, H. & Setiono, R. (1996). "A Probibilistic Approach to Feature Selection-A Filter Solution," Proceedings of the 13th International Conference on Machine Learning, ICML-96, Bari, Italy, pp. 319-327.
- Martin, J. D. (1994). "Goal-directed Clustering," Proceedings of the 1994 AAAI Spring Symposium on Goal- directed Learning.
Cited by
Sorted by citations
Girshick, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.169 · Cited by 28,105 · see citing works
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387 · see citing works
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570 · see citing works
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2019) · arXiv · doi:10.48550/arxiv.1910.10683 · Cited by 10,929 · see citing works
Natural Language Processing (almost) from ScratchPreprint
Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., & Kuksa, P. P. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1103.0398 · Cited by 6,882 · see citing works
Li, Z., & Hoiem, D. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2773081 · Cited by 4,202 · see citing works
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., & Darrell, T. (2013) · arXiv · doi:10.48550/arxiv.1310.1531 · Cited by 4,019 · see citing works
A Review on Multi-Label Learning Algorithms
Zhang, M.-L., & Zhou, Z. (2013) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2013.39 · Cited by 3,336 · see citing works
A Survey on Multi-Task Learning
Zhang, Y., & Yang, Q. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3070203 · Cited by 3,083 · see citing works
Generalizing from a Few Examples: A Survey on Few-shot Learning
Wang, Y., Yao, Q., Kwok, J. T., & Ni, L. M. (2021) · ACM Computing Surveys · doi:10.1145/3386252 · Cited by 3,165 · see citing works
BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning
Yu, F., Chen, H., Wang, X., Xian, W., Chen, Y., Liu, F., Madhavan, V., & Darrell, T. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00271 · Cited by 2,641 · see citing works
Ji, S., Xue, Y., & Carin, L. (2008) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2007.914345 · Cited by 2,406 · see citing works
Parameter-efficient transfer learning for NLP
Houlsby, N., Giurgiu, A., Jastrzebski, S., Morrone, B., de Laroussilhe, Q., Gesmundo, A., Attariyan, M., & Gelly, S. (2019) · arXiv · doi:10.48550/arxiv.1902.00751 · Cited by 2,270 · see citing works
Domain-specific language model pretraining for biomedical natural language processing
Gu, Y., Tinn, R., Cheng, H., Lucas, M., Usuyama, N., Liu, X., Naumann, T., Gao, J., & Poon, H. (2022) · ACM Transactions on Computing for Healthcare · doi:10.1145/3458754 · Cited by 2,170 · see citing works
QSAR Modeling: Where have you been? Where are you going to?
Cherkasov, A., Muratov, E. N., Fourches, D., Varnek, A., Baskin, I. I., Cronin, M., Dearden, J., Gramatica, P., Martin, Y. C., Todeschini, R., et al. (2014) · Journal of Medicinal Chemistry · doi:10.1021/jm4004285 · Cited by 2,164 · see citing works
Deep Learning for Image Super-Resolution: A Survey
Wang, Z., Chen, J., & Hoi, S. C. H. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.2982166 · Cited by 2,033 · see citing works
Toward an architecture for never-ending language learning
Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Hruschka, E., & Mitchell, T. (2010) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v24i1.7519 · Cited by 2,000 · see citing works
Boosting for transfer learning
Dai, W., Yang, Q., Xue, G.-R., & Yu, Y. H. (2007) · International Conference on Machine Learning · doi:10.1145/1273496.1273521 · Cited by 1,788 · see citing works
A Survey on Deep Learning for Named Entity Recognition
Li, J., Sun, A., Han, J., & Li, C. (2020) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.2981314 · Cited by 1,667 · see citing works
Convex multi-task feature learning
Argyriou, A., Evgeniou, T., & Pontil, M. (2008) · Machine Learning · doi:10.1007/s10994-007-5040-8 · Cited by 1,535 · see citing works
Transfer Learning for Reinforcement Learning Domains: A Survey
Taylor, M. E., & Stone, P. (2009) · doi:10.5555/1577069.1755839 · Cited by 1,521 · see citing works
Raina, R., Battle, A. J., Lee, H., Packer, B., & Ng, A. Y. (2007) · International Conference on Machine Learning · doi:10.1145/1273496.1273592 · Cited by 1,514 · see citing works
Cross-Stitch Networks for Multi-task Learning
Misra, I., Shrivastava, A., Gupta, A., & Hebert, M. (2016) · 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.2016.433 · Cited by 1,494 · see citing works
Regularized multi--task learning
Evgeniou, T., & Pontil, M. (2004) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1014052.1014067 · Cited by 1,473 · see citing works
Tsoumakas, G., Katakis, I., & Vlahavas, I. P. (2009) · Springer eBooks · doi:10.1007/978-0-387-09823-4_34 · Cited by 1,466 · see citing works
Facial Landmark Detection by Deep Multi-task Learning
Zhang, Z., Luo, P., Loy, C. C., & Tang, X. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10599-4_7 · Cited by 1,410 · see citing works
Deep learning-enabled medical computer vision
Esteva, A., Chou, K., Yeung, S., Naik, N., Madani, A., Mottaghi, A., Liu, Y., Topol, E., Dean, J., & Socher, R. (2021) · npj Digital Medicine · doi:10.1038/s41746-020-00376-2 · Cited by 1,406 · see citing works
A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection
Li, Q., Wen, Z., Wu, Z., Hu, S., Wang, N., Li, Y., Liu, X., & He, B. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3124599 · Cited by 1,407 · see citing works
Transformers in medical imaging: A survey
Shamshad, F., Khan, S., Zamir, S. W., Khan, M. H., Hayat, M., Khan, F. S., & Fu, H. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102802 · Cited by 1,412 · see citing works
A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data
Ando, R. K., & Zhang, T. (2005) · doi:10.5555/1046920.1194905 · Cited by 1,319 · see citing works
3D Human Pose Estimation in Video With Temporal Convolutions and Semi-Supervised Training
Pavllo, D., Feichtenhofer, C., Grangier, D., & Auli, M. (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.2019.00794 · Cited by 1,321 · see citing works
Describing objects by their attributes
Farhadi, A., Endres, I., Hoiem, D., & Forsyth, D. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206772 · Cited by 1,917 · see citing works
Sattler, F., Muller, K.-R., & Samek, W. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3015958 · Cited by 1,355 · see citing works
Instance-Aware Semantic Segmentation via Multi-task Network Cascades
Dai, J., He, K., & Sun, J. (2016) · 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.2016.343 · Cited by 1,251 · see citing works
Instance-aware Semantic Segmentation via Multi-task Network Cascades
Dai, J., He, K., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1512.04412 · Cited by 1,273 · see citing works
Federated Multi-Task LearningPreprint
Smith, V., Chiang, C.-K., Sanjabi, M., & Talwalkar, A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.10467 · Cited by 1,282 · see citing works
Deng, L., Hinton, G. E., & Kingsbury, B. E. D. (2013) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2013.6639344 · Cited by 1,222 · see citing works
Multi-Task Deep Neural Networks for Natural Language Understanding
Liu, X., He, P., Chen, W., & Gao, J. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1441 · Cited by 1,216 · see citing works
Toward Causal Representation Learning
Scholkopf, B., Locatello, F., Bauer, S., Ke, N. R., Kalchbrenner, N., Goyal, A., & Bengio, Y. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3058954 · Cited by 1,170 · see citing works
An overview of multi-task learning
Zhang, Y., & Yang, Q. (2017) · National Science Review · doi:10.1093/nsr/nwx105 · Cited by 1,120 · see citing works
A perspective view and survey of meta-learning
Vilalta, R., & Drissi, Y. (2002) · Artificial Intelligence Review · doi:10.1023/a:1019956318069 · Cited by 1,108 · see citing works
Multiple object tracking: A literature review
Luo, W., Xing, J., Milan, A., Zhang, X.-Q., Liu, W., Zhao, X., & Kim, T.-K. (2014) · Artificial Intelligence · doi:10.1016/j.artint.2020.103448 · Cited by 1,095 · see citing works
PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges
Unke, O. T., & Meuwly, M. (2019) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.9b00181 · Cited by 1,071 · see citing works
DeepTox: toxicity prediction using deep learning
Mayr, A., Klambauer, G., Unterthiner, T., & Hochreiter, S. (2016) · Frontiers in Environmental Science · doi:10.3389/fenvs.2015.00080 · Cited by 1,081 · see citing works
Recurrent Neural Network for Text Classification with Multi-Task LearningPreprint
Liu, P., Qiu, X., & Huang, X. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.05101 · Cited by 1,209 · see citing works
Federated learning on non-IID data: A survey
Zhu, H., Xu, J., Liu, S., & Jin, Y. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.07.098 · Cited by 1,062 · see citing works
Next-Generation Machine Learning for Biological Networks
Camacho, D. M., Collins, K. M., Powers, R. K., Costello, J. C., & Collins, J. J. (2018) · Cell · doi:10.1016/j.cell.2018.05.015 · Cited by 974 · see citing works
Generalizing to unseen domains: A survey on domain generalization
Wang, J., Lan, C., Liu, C., Ouyang, Y., Qin, T., Lu, W., Chen, Y., Zeng, W., & Yu, P. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3178128 · Cited by 1,046 · see citing works
A survey of unsupervised deep domain adaptation
Wilson, G., & Cook, D. J. (2020) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3400066 · Cited by 987 · see citing works
Transfer learning using computational intelligence: A survey
Jie Lu, B., Behbood, V., Peng, H., Zuo, H., Xue, S., & Zhang, G. (2015) · Knowledge-Based Systems · doi:10.1016/j.knosys.2015.01.010 · Cited by 971 · see citing works
Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation
Ghifary, M., Kleijn, W. B., Zhang, M., Balduzzi, D., & Li, W. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46493-0_36 · Cited by 948 · see citing works
Forecasting: theory and practice
Petropoulos, F., Apiletti, D., Assimakopoulos, V., Babai, M. Z., Barrow, D. K., Taieb, S. B., Bergmeir, C., Bessa, R., Bijak, J., Boylan, J., et al. (2020) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2021.11.001 · Cited by 630 · see citing works
Alzubaidi, L., Bai, J., Al-Sabaawi, A., Santamaría, J., Albahri, A. S., Al-dabbagh, B. S. N., Fadhel, M. A., Manoufali, M., Zhang, J., Al-Timemy, A. H., et al. (2023) · Journal Of Big Data · doi:10.1186/s40537-023-00727-2 · Cited by 992 · see citing works
Visual Domain Adaptation: A survey of recent advances
Patel, V. M., Gopalan, R., Li, R., & Chellappa, R. (2015) · IEEE Signal Processing Magazine · doi:10.1109/msp.2014.2347059 · Cited by 883 · see citing works
Meta-Learning in Neural Networks: A Survey
Hospedales, T. M., Antoniou, A., Micaelli, P., & Storkey, A. J. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3079209 · Cited by 886 · see citing works
Deep semantic segmentation of natural and medical images: a review
Taghanaki, S. A., Abhishek, K., Cohen, J., Cohen‐Adad, J., & Hamarneh, G. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-020-09854-1 · Cited by 888 · see citing works
Muratov, E. N., Bajorath, J., Sheridan, R. P., Tetko, I. V., Filimonov, D., Poroikov, V., Oprea, T. I., Baskin, I. I., Varnek, A., Roitberg, A., et al. (2020) · Chemical Society Reviews · doi:10.1039/d0cs00098a · Cited by 913 · see citing works
An Efficient Framework for Clustered Federated Learning
Ghosh, A., Chung, J., Yin, D., & Ramchandran, K. (2022) · IEEE Transactions on Information Theory · doi:10.1109/tit.2022.3192506 · Cited by 873 · see citing works
Domain Generalization by Solving Jigsaw Puzzles
Carlucci, F. M., D’Innocente, A., Bucci, S., Barbara Caputo, B., & Tommasi, T. (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.2019.00233 · Cited by 838 · see citing works
Multi-Task Learning for Dense Prediction Tasks: A Survey
Vandenhende, S., Georgoulis, S., Gansbeke, W. V., Proesmans, M., Dai, D., & Gool, L. V. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3054719 · Cited by 881 · see citing works
Domain Adaptation for Statistical Classifiers
Daume III, H., & Marcu, D. (2006) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1872 · Cited by 816 · see citing works
Salient Object Detection in the Deep Learning Era: An In-Depth Survey
Wang, W., Lai, Q., Fu, H., Shen, J., Ling, H., & Yang, R. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3051099 · Cited by 827 · see citing works
Complex Ratio Masking for Monaural Speech Separation
Williamson, D. S., Wang, Y., & Wang, D. (2015) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2015.2512042 · Cited by 799 · see citing works
Transfer learning using VGG-16 with Deep Convolutional Neural Network for Classifying Images
Tammina, S. (2019) · International Journal of Scientific and Research Publications · doi:10.29322/ijsrp.9.10.2019.p9420 · Cited by 787 · see citing works
Generalizing from a Few Examples: A Survey on Few-Shot LearningPreprint
Wang, Y., Yao, Q., Kwok, J. T.-Y., & Ni, L. M.-S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.05046 · Cited by 773 · see citing works
A community effort to assess and improve drug sensitivity prediction algorithms
Community, N. D., Costello, J. C., Heiser, L. M., Georgii, E., Gönen, M., Menden, M. P., Wang, N. J., Bansal, M., Ammad-ud-din, M., Hintsanen, P., et al. (2014) · Nature Biotechnology · doi:10.1038/nbt.2877 · Cited by 761 · see citing works
A Survey on Federated Learning for Resource-Constrained IoT Devices
Imteaj, A., Thakker, U., Wang, S., Li, J., & Amini, M. H. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3095077 · Cited by 774 · see citing works
Akçay, M. B., & Oğuz, K. (2019) · Speech Communication · doi:10.1016/j.specom.2019.12.001 · Cited by 743 · see citing works
A Distributional Perspective on Reinforcement LearningPreprint
Bellemare, M. G., Dabney, W., & Munos, R. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.06887 · Cited by 724 · see citing works
Machine learning and radiology
Wang, S., & Summers, R. M. (2012) · Medical Image Analysis · doi:10.1016/j.media.2012.02.005 · Cited by 705 · see citing works
Sharing Visual Features for Multiclass and Multiview Object Detection
Torralba, A. B., Murphy, K. P., & Freeman, W. T. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1055 · Cited by 695 · see citing works
Geng, X. (2016) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2016.2545658 · Cited by 696 · see citing works
Deep Neural Networks Improve Radiologists’ Performance in Breast Cancer Screening
Wu, N., Phang, J., Park, J., Shen, Y., Huang, Z., Zorin, M., Jastrzȩbski, S., Févry, T., Katsnelson, J., Kim, E. S., et al. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2945514 · Cited by 695 · see citing works
Designing neural networks through neuroevolution
Stanley, K. O., Clune, J., Lehman, J., & Miikkulainen, R. P. (2018) · Nature Machine Intelligence · doi:10.1038/s42256-018-0006-z · Cited by 702 · see citing works
A survey and critique of multiagent deep reinforcement learning
Hernández-Leal, P., Kartal, B., & Taylor, M. E. (2019) · Autonomous Agents and Multi-Agent Systems · doi:10.1007/s10458-019-09421-1 · Cited by 747 · see citing works
A Brief Review of Domain Adaptation
Farahani, A., Voghoei, S., Rasheed, K., & Arabnia, H. R. (2021) · Transactions on computational science and computational intelligence · doi:10.1007/978-3-030-71704-9_65 · Cited by 679 · see citing works
Universal Value Function Approximators
Schaul, T., Horgan, D., Gregor, K., & Silver, D. (2015) · Cited by 677 · see citing works
A survey on multi‐output regression
Borchani, H., Varando, G., Bielza, C., & Larrañaga, P. (2015) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1157 · Cited by 672 · see citing works
Domain Generalization for Object Recognition with Multi-task Autoencoders
Ghifary, M., Kleijn, W. B., Zhang, M., & Balduzzi, D. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.293 · Cited by 664 · see citing works
Li, Z., & Hoiem, D. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_37 · Cited by 659 · see citing works
Kokkinos, I. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.579 · Cited by 657 · see citing works
AdapterFusion: Non-Destructive Task Composition for Transfer Learning
Pfeiffer, J., Kamath, A., Rücklé, A., Cho, K., & Gurevych, I. (2021) · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume · doi:10.18653/v1/2021.eacl-main.39 · Cited by 636 · see citing works
“Multi-task learning (Caruana, 1997; Zhang and Yang, 2017; Liu et al., 2019a) is another approach for sharing information across multiple tasks.”
BackgroundEnd-to-End Autonomous Driving: Challenges and Frontiers
Chen, L., Wu, P., Chitta, K., Jaeger, B., Geiger, A., & Li, H. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3435937 · Cited by 673 · see citing works
Machine learning of molecular electronic properties in chemical compound space
Montavon, G., Rupp, M., Gobre, V., Vazquez-Mayagoitia, A., Hansen, K., Tkatchenko, A., Müller, K.-R., & von Lilienfeld, O. A. (2013) · New Journal of Physics · doi:10.1088/1367-2630/15/9/095003 · Cited by 631 · see citing works
Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations
Kiperwasser, E., & Goldberg, Y. (2016) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00101 · Cited by 614 · see citing works
Multi-task Deep Learning Based CT Imaging Analysis For COVID-19: Classification and Segmentation
Amyar, A., Modzelewski, R., Li, H., & Ruan, S. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.104037 · Cited by 615 · see citing works
Cross-language knowledge transfer using multilingual deep neural network with shared hidden layers
Huang, J.-T., Li, J., Yu, D., Deng, L., & Gong, Y. (2013) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2013.6639081 · Cited by 612 · see citing works
Gradient Surgery for Multi-Task Learning
Yu, T., Kumar, S., Gupta, A., Levine, S., Hausman, K., & Finn, C. (2020) · arXiv · doi:10.48550/arxiv.2001.06782
Domain Adaptation Problems: A DASVM Classification Technique and a Circular Validation Strategy
Bruzzone, L., & Marconcini, M. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.57 · Cited by 591 · see citing works
Large-scale comparison of machine learning methods for drug target prediction on ChEMBL
Mayr, A., Klambauer, G., Unterthiner, T., Steijaert, M., Wegner, J. K., Ceulemans, H., Clevert, D.-A., & Hochreiter, S. (2018) · Chemical Science · doi:10.1039/c8sc00148k · Cited by 584 · see citing works
A review of object detection based on deep learning
Xiao, Y., Tian, Z., Yu, J., Zhang, Y., Liu, S., Du, S., & Lan, X. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-08976-6 · Cited by 584 · see citing works
A model of inductive bias learning
Baxter, J. (2000) · Journal of Artificial Intelligence Research · doi:10.1613/jair.731 · Cited by 582 · see citing works
Sharing features: efficient boosting procedures for multiclass object detection
Torralba, A. B., Murphy, K. P., & Freeman, W. T. (2004) · 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.2004.1315241 · Cited by 581 · see citing works
Deep Learning via Semi-supervised Embedding
Weston, J., Ratle, F., Mobahi, H., & Collobert, R. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-35289-8_34 · Cited by 577 · see citing works
Caruana, R. (1998) · Learning to Learn · doi:10.1007/978-1-4615-5529-2_5 · Cited by 577 · see citing works
Self-citationMulti-Way, Multilingual Neural Machine Translation with a Shared Attention Mechanism
Fırat, O., Cho, K., & Bengio, Y. (2016) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n16-1101 · Cited by 570 · see citing works
A comprehensive survey on pretrained foundation models: a history from BERT to ChatGPT
Zhou, C., Li, Q., Li, C., Yu, J., Liu, Y., Wang, G., Zhang, K., Ji, C., Yan, Q., He, L., et al. (2024) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-024-02443-6 · Cited by 704 · see citing works
Mater, A. C., & Coote, M. L. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00266 · Cited by 565 · see citing works
Transfer learning: a friendly introduction
Hosna, A., Merry, E., Gyalmo, J., Alom, Z., Aung, Z., & Azim, M. A. (2022) · Journal Of Big Data · doi:10.1186/s40537-022-00652-w · Cited by 617 · see citing works
Multitask Prompted Training Enables Zero-Shot Task Generalization
Sanh, V., Webson, A., Raffel, C., Bach, S. H., Sutawika, L., Alyafeai, Z., Chaffin, A., Stiegler, A., Scao, T. L., Raja, A., et al. (2021) · arXiv · doi:10.48550/arxiv.2110.08207 · Cited by 612 · see citing works
- Authors
- Caruana, R.
- Affiliations
- Carnegie Mellon University (US)
- Corresponding author
- Caruana, Rich
- ORCID
- Caruana, Rich
- Venue
- Machine Learning
- Published
- 1997-07
- Volume
- 28
- Issue
- 1
- Pages
- 41-75
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W2913340405
- Cited by
- 6,636
- References
- 67
- Citations by year
- 2026: 532 · 2025: 617 · 2024: 510 · 2023: 505 · 2022: 565 · 2021: 803 · 2020: 767 · 2019: 563 · 2018: 323 · 2017: 259 · 2016: 188 · 2015: 151 · 2014: 124 · 2013: 89 · 2012: 136
- FWCI
- 5.68
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Machine Learning (publishedVersion)
- Funders
- National Science Foundation (9315428)
- University of Pittsburgh
- Advanced Research Projects Agency
- 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 › Reinforcement Learning in Robotics
- Keywords
- multi-task learning, inductive transfer, inductive bias, shared representations, task relatedness, k-nearest neighbors, kernel regression, case-based learning, decision tree
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