The DOI matches its Crossref record; 64 of the 67 references it cites were found in a registry or an index.
Multitask Learning
Open accessOverview
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.
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“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]
The cited source says what the claim attributes to it.
In the citing paper
“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
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“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]
Experimental — accuracy not yet measuredIn the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
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
(1992) · Cited by 23,431
Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439
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 17,385
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
Statistical 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
Causation, Prediction, and Search
Spirtes, P., Glymour, C., & Scheines, R. (2001) · doi:10.7551/mitpress/1754.001.0001 · Cited by 4,521
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
Flagged first, then by citations
3 of the 67 references couldn’t be matched to a known paper.
Research works printed but not linked: 5
- 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
Items 501–600 of 1,024
Ramapuram, J., Gregorová, M., & Kalousis, A. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.02.115 · Cited by 131
Predicting malignant nodules by fusing deep features with classical radiomics features
Paul, R., Hawkins, S. H., Schabath, M. B., Gillies, R. J., Hall, L., & Goldgof, D. B. (2018) · Journal of medical imaging · doi:10.1117/1.jmi.5.1.011021 · Cited by 111
Multi-target prediction: a unifying view on problems and methods
Waegeman, W., Dembczyński, K., & Hüllermeier, E. (2018) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-018-0595-5 · Cited by 107
A feature transfer enabled multi-task deep learning model on medical imaging
Gao, F., Yoon, H., Wu, T., & Chu, X. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.112957 · Cited by 111
Dai, W., Jin, O., Xue, G.-R., Yang, Q., & Yu, Y. H. (2009) · International Conference on Machine Learning · doi:10.1145/1553374.1553399 · Cited by 108
An Improved Multi-task Learning Approach with Applications in Medical Diagnosis
Bi, J., Xiong, T., Yu, S., Dündar, M., & Rao, R. B. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-87479-9_26 · Cited by 107
An Empirical Study of Multi-Task Learning on BERT for Biomedical Text Mining
Peng, Y., Chen, Q., & Lu, Z. (2020) · Workshop on Biomedical Language Processing (BioNLP) · doi:10.18653/v1/2020.bionlp-1.22 · Cited by 122
Reinforcement and deep reinforcement learning for wireless Internet of Things: A survey
Frikha, M. S., Gammar, S. M., Lahmadi, A., & Andrey, L. (2021) · Computer Communications · doi:10.1016/j.comcom.2021.07.014 · Cited by 113
On-Road Driver Emotion Recognition Using Facial Expression
Xiao, H., Li, W., Zeng, G., Wu, Y., Xue, J., Zhang, J., Li, C., & Guo, G. (2022) · Applied Sciences · doi:10.3390/app12020807 · Cited by 112
Efficient Methods for Natural Language Processing: A Survey
Treviso, M., Lee, J.-U., Ji, T., van Aken, B., Cao, Q., Ciosici, M. R., Hassid, M., Heafield, K., Hooker, S., Raffel, C., et al. (2023) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00577 · Cited by 156
Few-shot Learning: A SurveyPreprint
Wang, Y., & Yao, Q. (2019) · arXiv (Cornell University) · Cited by 112
Multitask learning improves prediction of cancer drug sensitivity
Yuan, H., Paskov, I., Paskov, H. S., González, A. J., & Leslie, C. S. (2016) · Scientific Reports · doi:10.1038/srep31619 · Cited by 104
Deep learning methods for biomedical named entity recognition: a survey and qualitative comparison
Song, B., Li, F., Liu, Y., & Zeng, X. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab282 · Cited by 112
A Generative Model to Synthesize EEG Data for Epileptic Seizure Prediction
Rasheed, K., Qadir, J., O'Brien, T. J., Kuhlmann, L., & Razi, A. (2021) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2021.3125023 · Cited by 4
Multiobjective Multitask Optimization-Neighborhood as a Bridge for Knowledge Transfer
Wang, X., Dong, Z., Tang, L., & Zhang, Q. (2022) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2022.3154416 · Cited by 106
Prediction of peptide mass spectral libraries with machine learning
Cox, J. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01424-w · Cited by 109
A Generic Quality Control Framework for Fetal Ultrasound Cardiac Four-Chamber Planes
Dong, J., Liu, S., Liao, Y., Wen, H., Lei, B., Li, S., & Wang, T. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2019.2948316 · Cited by 104
Learning Distributed Representations from Reviews for Collaborative Filtering
Almahairi, A., Kastner, K., Cho, K., & Courville, A. C. (2015) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/2792838.2800192 · Cited by 109
Varifocal-Net: A Chromosome Classification Approach Using Deep Convolutional Networks
Qin, Y., Wen, J., Zheng, H., Huang, X., Yang, J., Song, N., Zhu, Y., Wu, L., & Yang, G. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2905841 · Cited by 108
Multi-task Domain Adaptation for Sequence Tagging
Peng, N., & Dredze, M. H. (2017) · Workshop on Representation Learning for NLP (RepL4NLP) · doi:10.18653/v1/w17-2612 · Cited by 118
Automatic detection of rare pathologies in fundus photographs using few-shot learning
Quellec, G., Lamard, M., Conze, P.-H., Massin, P., & Cochener, B. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101660 · Cited by 105
Deep learning-based breast cancer grading and survival analysis on whole-slide histopathology images
Wetstein, S. C., de Jong, V. M. T., Stathonikos, N., Opdam, M., Dackus, G. M. H. E., Pluim, J. P. W., van Diest, P. J., & Veta, M. (2022) · Scientific Reports · doi:10.1038/s41598-022-19112-9 · Cited by 110
Recent Advances in Large Language Models for Healthcare
Nassiri, K., & Akhloufi, M. A. (2024) · BioMedInformatics · doi:10.3390/biomedinformatics4020062 · Cited by 108
Joint Sparse Representation and Multitask Learning for Hyperspectral Target Detection
Zhang, Y., Du, B., Zhang, L., & Liu, T. (2016) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2016.2616649 · Cited by 104
Weng, S., Xu, X., Li, J., & Wong, S. T. C. (2017) · Journal of Biomedical Optics · doi:10.1117/1.jbo.22.10.106017 · Cited by 104
A hybrid of genetic transform and hyper-rectangle search strategies for evolutionary multi-tasking
Liang, Z., Zhang, J., Feng, L., & Zhu, Z. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.07.015 · Cited by 104
Hallucinating IDT Descriptors and I3D Optical Flow Features for Action Recognition With CNNs
Wang, L., Koniusz, P., & Huynh, D. Q. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00879 · Cited by 106
Balamuralidhar, N., Tilon, S., & Nex, F. (2021) · Remote Sensing · doi:10.3390/rs13040573 · Cited by 104
Zhang, X. (2020) · Springer eBooks · doi:10.1007/978-981-15-2770-8_6 · Cited by 118
Trace Norm Regularised Deep Multi-Task LearningPreprint
Yang, Y., & Hospedales, T. M. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.04038 · Cited by 104
Deep learning in electron microscopy
Ede, J. M. (2020) · Machine Learning Science and Technology · doi:10.1088/2632-2153/abd614 · Cited by 118
A multitask framework to detect depression, sentiment and multi-label emotion from suicide notes
Ghosh, S., Ekbal, A., & Bhattacharyya, P. (2022) · Cognitive Computation · doi:10.1007/s12559-021-09828-7 · Cited by 102
Can humans fly? Action understanding with multiple classes of actors
Xu, C., Hsieh, S.-H., Xiong, C., & Corso, J. J. (2015) · 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.2015.7298839 · Cited by 98
Drug susceptibility prediction against a panel of drugs using kernelized Bayesian multitask learning
Gönen, M., & Margolin, A. A. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu464 · Cited by 102
Multiobjective Multitasking Optimization Based on Incremental Learning
Lin, J., Liu, H., Xue, B., Zhang, M., & Gu, F. (2019) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2019.2962747 · Cited by 102
A Survey on Securing Federated Learning: Analysis of Applications, Attacks, Challenges, and Trends
Neto, H. N. C., Hribar, J., Dusparić, I., Mattos, D. M. F., & Fernandes, N. C. (2023) · IEEE Access · doi:10.1109/access.2023.3269980 · Cited by 114
AI-assisted discovery of high-temperature dielectrics for energy storage
Gurnani, R., Shukla, S., Kamal, D., Wu, C., Hao, J., Kuenneth, C., Aklujkar, P., Khomane, A. R., Daniels, R., Deshmukh, A. A., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-50413-x · Cited by 120
Deep networks for system identification: A survey
Pillonetto, G., Aravkin, A. Y., Gedon, D., Lennart Ljung, A., Ribeiro, A. H., & Schön, T. B. (2024) · Automatica · doi:10.1016/j.automatica.2024.111907 · Cited by 153
Tran, T., & Kavuluru, R. (2017) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2017.06.010 · Cited by 101
Huang, S., Zhong, J., & Yu, W. (2019) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2019.2945775 · Cited by 101
Advances and Perspectives in Applying Deep Learning for Drug Design and Discovery
Lipinski, C. F., Maltarollo, V. G., Oliveira, P. R., da Silva, A. B. F., & Honório, K. M. (2019) · Frontiers in Robotics and AI · doi:10.3389/frobt.2019.00108 · Cited by 106
Learning to Few-Shot Learn Across Diverse Natural Language Classification Tasks
Bansal, T., Jha, R., & McCallum, A. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.448 · Cited by 125
Datasets for Cognitive Load Inference Using Wearable Sensors and Psychological Traits
Gjoreski, M., Kolenik, T., Knez, T., Luštrek, M., Gams, M., Gjoreski, H., & Pejović, V. (2020) · Applied Sciences · doi:10.3390/app10113843 · Cited by 111
Wang, J., Zheng, Y., Ma, J., Li, X., Wang, C., Gee, J. C., Wang, H., & Huang, W. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102687 · Cited by 105
DELTA: DEep Learning Transfer using Feature Map with Attention for Convolutional NetworksPreprint
Li, X., Xiong, H., Wang, H., Rao, Y., Liu, L., Chen, Z., & Huan, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.09229 · Cited by 117
Namburete, A. I. L., Xie, W., Yaqub, M., Zisserman, A., & Noble, J. A. (2018) · Medical Image Analysis · doi:10.1016/j.media.2018.02.006 · Cited by 95
A notion of task relatedness yielding provable multiple-task learning guarantees
Ben-David, S., & Schuller, R. (2008) · Machine Learning · doi:10.1007/s10994-007-5043-5 · Cited by 100
Nonnegative shared subspace learning and its application to social media retrieval
Gupta, S., Phung, D., Adams, B., Tran, T., & Venkatesh, S. (2010) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1835804.1835951 · Cited by 100
Transfer metric learning by learning task relationships
Zhang, Y., & Yeung, D. (2010) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1835804.1835954 · Cited by 100
Deep-Learning Domain Adaptation Techniques for Credit Cards Fraud Detection
Lebichot, B., Borgne, Y. L., He-Guelton, L., Oblé, F., & Bontempi, G. (2019) · Proceedings of the international neural networks society · doi:10.1007/978-3-030-16841-4_8 · Cited by 100
A Latent Variable Recurrent Neural Network for Discourse-Driven Language Models
Ji, Y., Haffari, G., & Eisenstein, J. (2016) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n16-1037 · Cited by 141
Multimodal Summarization with Guidance of Multimodal Reference
Zhu, J., Zhou, Y., Zhang, J., Li, H., Zong, C., & Li, C. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6525 · Cited by 101
Exploring Versatile Generative Language Model Via Parameter-Efficient Transfer Learning
Lin, Z., Madotto, A., & Fung, P. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.41 · Cited by 163
Decuyper, M., Bonte, S., Deblaere, K., & Holen, R. V. (2020) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2020.101831 · Cited by 104
Ren, H., Ruan, S., Li, Y., Bao, J., Meng, C., Li, R., & Zheng, Y. (2021) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3447548.3467238 · Cited by 100
A Multi-Task Learning Approach to Hate Speech Detection Leveraging Sentiment Analysis
Plaza-del-Arco, F. M., Molina-González, M. D., López, L. A. U., & Valdivia, M. T. M. (2021) · IEEE Access · doi:10.1109/access.2021.3103697 · Cited by 108
Mod-Squad: Designing Mixtures of Experts As Modular Multi-Task Learners
Chen, Z., Shen, Y., Ding, M., Chen, Z., Zhao, H., Miller, E. G., & Gan, C. (2023) · 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/cvpr52729.2023.01138 · Cited by 100
Training, Selection, and Robust Calibration of Retention Time Models for Targeted Proteomics
Moruz, L., Tomazela, D. M., & Käll, L. (2010) · Journal of Proteome Research · doi:10.1021/pr1005058 · Cited by 95
MutantX-S: scalable malware clustering based on static features
Hu, X. Y., Bhatkar, S., Griffin, K., & Shin, K. G. (2013) · Cited by 99
Learning Graph Embeddings for Open World Compositional Zero-Shot Learning
Mancini, M., Naeem, M. F., Xian, Y., & Akata, Z. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3163667 · Cited by 101
Universal representations:The missing link between faces, text, planktons, and cat breedsPreprint
Bilen, H., & Vedaldi, A. (2017) · Oxford University Research Archive (ORA) (University of Oxford) · doi:10.48550/arxiv.1701.07275 · Cited by 116
WT5?! Training Text-to-Text Models to Explain their PredictionsPreprint
Narang, S., Raffel, C., Lee, K., Roberts, A., Fiedel, N., & Malkan, K. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.14546 · Cited by 221
Learning to Branch for Multi-Task LearningPreprint
Guo, P., Lee, C., & Ulbricht, D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.01895 · Cited by 99
Cluster Prototypes and Fuzzy Memberships Jointly Leveraged Cross-Domain Maximum Entropy Clustering
Qian, P., Jiang, Y., Deng, Z., Hu, L., Sun, S., Wang, S., & Muzic, R. F. (2015) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2015.2399351 · Cited by 98
Multi-Task Label Embedding for Text Classification
Zhang, H., Xiao, L., Chen, W., Wang, Y., & Jin, Y. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1484 · Cited by 102
Imbalanced Continual Learning with Partitioning Reservoir Sampling
Kim, C. D., Jeong, J., & Kim, G. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58601-0_25 · Cited by 101
Peirelinck, T., Kazmi, H., Mbuwir, B. V., Hermans, C., Spiessens, F., Suykens, J. A. K., & Deconinck, G. (2021) · Energy and AI · doi:10.1016/j.egyai.2021.100126 · Cited by 102
Empirical Bayes for Learning to Learn
Heskes, T. (2000) · Radboud Repository (Radboud University) · doi:10.5555/645529.658133 · Cited by 98
Collaborative Filtering on a Family of Biological Targets
Erhan, D., L'Heureux, P., Yue, S. Y., & Bengio, Y. (2006) · Journal of Chemical Information and Modeling · doi:10.1021/ci050367t · Cited by 100
Learning to Learn Causal Models
Kemp, C., Goodman, N. D., & Tenenbaum, J. B. (2010) · Cognitive Science · doi:10.1111/j.1551-6709.2010.01128.x · Cited by 104
Exploiting Unrelated Tasks in Multi-Task Learning
Romera‐Paredes, B., Argyriou, A. A., Bianchi‐Berthouze, N., & Pontil, M. (2012) · Cited by 98
Few-Shot Learning via Learning the Representation, ProvablyPreprint
Du, S. S., Hu, W. P., Kakade, S. M., Lee, J. D., & Lei, Q. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.09434 · Cited by 98
Multitask Learning for Fine-Grained Twitter Sentiment Analysis
Balikas, G., Moura, S., & Amini, M.-R. (2017) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3077136.3080702 · Cited by 101
Zhang, X. S., Tang, F., Dodge, H. H., Zhou, J., & Wang, F. (2019) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3292500.3330779 · Cited by 102
Multitask Learning Over Graphs: An Approach for Distributed, Streaming Machine Learning
Nassif, R., Vlaski, S., Richard, C., Chen, J., & Sayed, A. H. (2020) · IEEE Signal Processing Magazine · doi:10.1109/msp.2020.2966273 · Cited by 101
Cluster-driven Graph Federated Learning over Multiple Domains
Caldarola, D., Mancini, M., Galasso, F., Ciccone, M., Rodolà, E., & Barbara Caputo, B. (2021) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00309 · Cited by 102
Flexible latent variable models for multi-task learning
Zhang, J., Zoubin Ghahramani, A., & Yang, Y. (2008) · Machine Learning · doi:10.1007/s10994-008-5050-1 · Cited by 97
Lim, G., Bellemo, V., Xie, Y., Lee, X. Q., Yip, M. Y. T., & Ting, D. S. W. (2020) · Eye and Vision · doi:10.1186/s40662-020-00182-7 · Cited by 96
Learning Multi-Level Task Groups in Multi-Task Learning
Han, L., & Zhang, Y. (2015) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v29i1.9581 · Cited by 96
Luo, J., Gupta, A., Ong, Y.-S., & Wang, Z. (2018) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2018.2811761 · Cited by 96
Hierarchical Lifelong Learning by Sharing Representations and Integrating Hypothesis
Zhang, T., Su, G., Qing, C., Xu, X., Cai, B., & Xing, X. (2019) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2018.2884996 · Cited by 97
Machine learning the thermodynamic arrow of time
Seif, A., Hafezi, M., & Jarzynski, C. (2020) · Nature Physics · doi:10.1038/s41567-020-1018-2 · Cited by 102
Zuheros, C., Martínez‐Cámara, E., Herrera‐Viedma, E., & Herrera, F. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.10.019 · Cited by 99
A Survey on Deep Learning and Explainability for Automatic Report Generation from Medical Images
Messina, P., Pino, P., Parra, D., Soto, Á., Besa, C., Uribe, S. A., Andía, M. E., Cristián Tejos, N., Prieto, C., & Daniel Capurro, N. (2022) · ACM Computing Surveys · doi:10.1145/3522747 · Cited by 111
Kernel analysis of deep networks
Montavon, G., Braun, M. L., & Müller, K. (2011) · Cited by 97
Shift-Invariance Sparse Coding for Audio ClassificationPreprint
Grosse, R., Raina, R., Kwong, H., & Ng, A. Y. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1206.5241 · Cited by 114
Branched Multi-Task Networks: Deciding what layers to share
Vandenhende, S., Georgoulis, S., Gool, L. V., & Brabandere, B. D. (2020) · British Machine Vision Conference (BMVC) · doi:10.5244/c.34.59 · Cited by 95
Deep Single Image Camera Calibration With Radial Distortion
Lopez, M., Marí, R., Gargallo, P., Kuang, Y., González-Jiménez, J., & Haro, G. (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.2019.01209 · Cited by 95
End-to-End Audiovisual Speech Recognition System With Multitask Learning
Tao, F., & Busso, C. (2020) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2020.2975922 · Cited by 95
Multi-Instance Pose Networks: Rethinking Top-Down Pose Estimation
Khirodkar, R., Chari, V., Agrawal, A., & Tyagi, A. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00311 · Cited by 100
Federated Multi-Task Learning for Joint Diagnosis of Multiple Mental Disorders on MRI Scans
Huang, Z.-A., Hu, Y. F., Liu, R., Xue, X., Zhu, Z., Song, L., & Tan, K. C. (2022) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2022.3210940 · Cited by 102
Routing Networks: Adaptive Selection of Non-linear Functions for Multi-Task LearningPreprint
Rosenbaum, C., Klinger, T., & Riemer, M. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.01239 · Cited by 171
Multi-task Learning of Deep Neural Networks for Low-resource Speech Recognition
Chen, D., & Mak, B. K.-W. (2015) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2015.2422573 · Cited by 94
A survey on computer aided diagnosis for ocular diseases
Zhang, Z., Srivastava, R., Liu, H., Chen, X., Duan, L., Wong, D. W. K., Kwoh, C. K., Wong, T. Y., & Liu, J. (2014) · BMC Medical Informatics and Decision Making · doi:10.1186/1472-6947-14-80 · Cited by 92
Multi-task learning for the prediction of wind power ramp events with deep neural networks
Dorado-Moreno, M., Navarin, N., Gutiérrez, P. A., Prieto-Godino, L., Sperduti, A., Salcedo‐Sanz, S., & Hervás‐Martínez, C. (2020) · Neural Networks · doi:10.1016/j.neunet.2019.12.017 · Cited by 94
Linkon, A. H. M., Labib, M. M., Hasan, T., Hossain, M. T., & Marium-E-Jannat (2021) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2021.100582 · Cited by 104
Abubakar, A., Almeida, C. F. M., & Gemignani, M. M. F. (2021) · Machines · doi:10.3390/machines9120328 · Cited by 94
Smyl, S., Dudek, G., & Pełka, P. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3259149 · Cited by 112
Alzheimer's Disease Diagnostics by a Deeply Supervised Adaptable 3D Convolutional NetworkPreprint
Hosseini-Asl, E., Gimel’farb, G., & El-Baz, A. S. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1607.00556 · Cited by 168
Graph Meta Learning via Local SubgraphsPreprint
Huang, K., & Žitnik, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.07889 · Cited by 101
Metadata
- 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
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
