Cited by 577Domain Adaptation and Few-Shot LearningMachine Learning and AlgorithmsNeural Networks and Applications

The DOI matches its Crossref record; 51 of the 52 references it cites were found in a registry or an index.

Multitask Learning

Caruana, Rich·1998·Learning to Learn

DOI 10.1007/978-1-4615-5529-2_5

Open this paper in CoChat — free account

Integrity signals

Registry

Crossref · 10.1007/978-1-4615-5529-2_5

Cites

51 of 51 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

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
Induction of decision trees
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
Multitask Learning
Caruana, R. (1997) · Machine Learning · doi:10.1023/a:1007379606734 · Cited by 6,636
Self-citation
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
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

Show all 52 references

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.

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
HyperFace: A Deep Multi-Task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognition
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
Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning
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
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
Toward a rational and mechanistic account of mental effort
Shenhav, A., Musslick, S., Lieder, F., Kool, W., Griffiths, T. L., Cohen, J. D., & Botvinick, M. M. (2017) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-072116-031526 · Cited by 1,077
Neural Network Methods for Natural Language Processing
Goldberg, Y. (2017) · Synthesis Lectures on Human Language Technologies · doi:10.1007/978-3-031-02165-7 · Cited by 821
A New Representation of Skeleton Sequences for 3D Action Recognition
Ke, Q., Bennamoun, M., An, S., Sohel, F., & Boussaid, F. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.486 · Cited by 793
Pattern classification with missing data: a review
García-Laencina, P. J., Sancho‐Gómez, J., & Figueiras-Vidal, A. R. (2009) · Neural Computing and Applications · doi:10.1007/s00521-009-0295-6 · Cited by 766
Online learning: A comprehensive survey
Hoi, S. C. H., Sahoo, D., Lu, J., & Zhao, P. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.04.112 · Cited by 913
Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights
Mallya, A., Davis, D., & Lazebnik, S. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01225-0_5 · Cited by 673
Expert Gate: Lifelong Learning with a Network of Experts
Aljundi, R., Chakravarty, P., & Tuytelaars, T. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.753 · Cited by 619
A new representation of skeleton sequences for 3D action recognition
Ke, Q., Bennamoun, M., An, S., Sohel, F., & Boussaïd, F. (2017) · UWA Profiles and Research Repository (UWA) · Cited by 480
An All-In-One Convolutional Neural Network for Face Analysis
Ranjan, R., Sankaranarayanan, S., Castillo, C. D., & Chellappa, R. (2017) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2017.137 · Cited by 417
Insights on Transfer Optimization: Because Experience is the Best Teacher
Gupta, A., Ong, Y.-S., & Feng, L. (2017) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2017.2769104 · Cited by 379
Deep neural networks in psychiatry
Durstewitz, D., Koppe, G., & Meyer‐Lindenberg, A. (2019) · Molecular Psychiatry · doi:10.1038/s41380-019-0365-9 · Cited by 297
Deep Architecture for High-Speed Railway Insulator Surface Defect Detection: Denoising Autoencoder With Multitask Learning
Kang, G., Gao, S., Yu, L., & Zhang, D. (2018) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2018.2868490 · Cited by 272
Learning Clip Representations for Skeleton-Based 3D Action Recognition
Ke, Q., Bennamoun, M., An, S., Sohel, F. A., & Boussaïd, F. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2812099 · Cited by 271
A brief review on multi-task learning
Thung, K., & Chong-yaw Wee, A. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-6463-x · Cited by 266
REBEL: Relation Extraction By End-to-end Language generation
Huguet Cabot, P.-L., & Navigli, R. (2021) · Findings of the Association for Computational Linguistics: EMNLP 2021 · doi:10.18653/v1/2021.findings-emnlp.204 · Cited by 261
Deep neural networks employing Multi-Task Learning and stacked bottleneck features for speech synthesis
Wu, Z., Valentini-Botinhao, C., Watts, O., & King, S. (2015) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2015.7178814 · Cited by 242
Domain Adaptation: Challenges, Methods, Datasets, and Applications
Singhal, P., Walambe, R. A., Ramanna, S., & Kotecha, K. V. (2023) · IEEE Access · doi:10.1109/access.2023.3237025 · Cited by 276
From Red Wine to Red Tomato: Composition with Context
Misra, I., Gupta, A., & Hebert, M. (2017) · 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.2017.129 · Cited by 248
Demystifying Multitask Deep Neural Networks for Quantitative Structure–Activity Relationships
Xu, Y., Ma, J., Liaw, A., Sheridan, R. P., & Svetnik, V. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00087 · Cited by 238
Multi-task transfer learning deep convolutional neural network: application to computer-aided diagnosis of breast cancer on mammograms
Samala, R. K., Chan, H., Hadjiiski, L. M., Helvie, M. A., Kenny, H., & Richter, C. (2017) · Physics in Medicine and Biology · doi:10.1088/1361-6560/aa93d4 · Cited by 231
Detect Rumor and Stance Jointly by Neural Multi-task Learning
Ma, J., Gao, W. G., & Wong, K. (2018) · International World Wide Web Conference (WWW) · doi:10.1145/3184558.3188729 · Cited by 234
Borrowing Treasures from the Wealthy: Deep Transfer Learning through Selective Joint Fine-Tuning
Ge, W., & Yu, Y. (2017) · 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.2017.9 · Cited by 226
Learning Temporal and Spatial Correlations Jointly: A Unified Framework for Wind Speed Prediction
Zhu, Q., Chen, J., Shi, D., Zhu, L., Bai, X., Duan, X., & Liu, Y. (2019) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2019.2897136 · Cited by 225
Multiple-target deep learning for LSTM-RNN based speech enhancement
Sun, L., Du, J., Dai, L.-R., & Lee, C. (2017) · Joint Workshop on Hands-free Speech Communication and Microphone Arrays (HSCMA) · doi:10.1109/hscma.2017.7895577 · Cited by 220
Encoder Based Lifelong Learning
Rannen, A., Aljundi, R., Blaschko, M. B., & Tuytelaars, T. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.148 · Cited by 324
A Machine Learning Approach for Air Quality Prediction: Model Regularization and Optimization
Zhu, D., Cai, C., Yang, T., & Zhou, X. S. (2018) · Big Data and Cognitive Computing · doi:10.3390/bdcc2010005 · Cited by 217
Management of Thyroid Nodules Seen on US Images: Deep Learning May Match Performance of Radiologists
Buda, M., Wildman‐Tobriner, B., Hoang, J. K., Thayer, D., Tessler, F. N., Middleton, W. D., & Mazurowski, M. A. (2019) · Radiology · doi:10.1148/radiol.2019181343 · Cited by 215
Neural Approaches to Conversational AI
Gao, J., Galley, M., & Li, L. (2019) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000074 · Cited by 424
Learning models with uniform performance via distributionally robust optimization
Duchi, J. C., & Namkoong, H. (2021) · The Annals of Statistics · doi:10.1214/20-aos2004 · Cited by 335
Gene regulatory network reconstruction using single-cell RNA sequencing of barcoded genotypes in diverse environments
Jackson, C. A., Castro, D. M., Saldi, G.-A., Bonneau, R., & Gresham, D. (2020) · eLife · doi:10.7554/elife.51254 · Cited by 174
Deep Learning Object Detection Methods for Ecological Camera Trap Data
Schneider, S., Taylor, G. W., & Kremer, S. C. (2018) · Canadian Conference on Computer and Robot Vision · doi:10.1109/crv.2018.00052 · Cited by 214
UniverSeg: Universal Medical Image Segmentation
Butoi, V. I., Gonzalez Ortiz, J. J., Ma, T., Sabuncu, M. R., Guttag, J., & Dalca, A. V. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01960 · Cited by 195
Lifelong Learning via Progressive Distillation and Retrospection
Hou, S., Pan, X., Loy, C. C., Wang, Z., & Lin, D. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01219-9_27 · Cited by 190
Transfer learning for biomedical named entity recognition with neural networks
Giorgi, J., & Bader, G. D. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty449 · Cited by 193
Linearized domain adaptation in evolutionary multitasking
Bali, K. K., Gupta, A., Feng, L., Ong, Y.-S., & Siew, T. P. (2017) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2017.7969454 · Cited by 183
Evolutionary Multitasking With Dynamic Resource Allocating Strategy
Gong, M., Tang, Z., Li, H., & Zhang, J. (2019) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2019.2893614 · Cited by 177
Survey of Deep Representation Learning for Speech Emotion Recognition
Latif, S., Rana, R. K., Khalifa, S., Jurdak, R., Qadir, J., & Schuller, B. W. (2021) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2021.3114365 · Cited by 193
Multifactorial Genetic Programming for Symbolic Regression Problems
Zhong, J., Feng, L., Cai, W., & Ong, Y.-S. (2018) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2018.2853719 · Cited by 167
Multimodal Task-Driven Dictionary Learning for Image Classification
Bahrampour, S., Nasrabadi, N. M., Ray, A., & Jenkins, W. K. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2496275 · Cited by 161
A Review of Deep Learning Based Speech Synthesis
Ning, Y., He, S., Wu, Z., Xing, C., & Zhang, L. (2019) · Applied Sciences · doi:10.3390/app9194050 · Cited by 164
An Effective Knowledge Transfer Approach for Multiobjective Multitasking Optimization
Lin, J., Liu, H., Tan, K. C., & Gu, F. (2020) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2020.2969025 · Cited by 163
Three critical factors affecting automated image species recognition performance for camera traps
Schneider, S., Greenberg, S., Taylor, G. W., & Kremer, S. C. (2020) · Ecology and Evolution · doi:10.1002/ece3.6147 · Cited by 182
Quantitative Toxicity Prediction Using Topology Based Multitask Deep Neural Networks
Wu, K., & Wei, G.-W. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00558 · Cited by 160
Expert Level Control of Ramp Metering Based on Multi-Task Deep Reinforcement Learning
Belletti, F., Haziza, D., Gomes, G., & Bayen, A. M. (2017) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2017.2725912 · Cited by 167
Lifelong Machine Learning
Chen, Z., & Liu, B. (2018) · Synthesis Lectures on Artificial Intelligence and Machine Learning · doi:10.2200/s00737ed1v01y201610aim033 · Cited by 154
No-Frills Human-Object Interaction Detection: Factorization, Layout Encodings, and Training Techniques
Gupta, T., Schwing, A. G., & Hoiem, D. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00977 · Cited by 145
Learn on Source, Refine on Target: A Model Transfer Learning Framework with Random Forests
Segev, N., Harel, M., Mannor, S., Crammer, K., & El‐Yaniv, R. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2618118 · Cited by 140
ML-Decoder: Scalable and Versatile Classification Head
Ridnik, T., Sharir, G., Ben-Cohen, A., Ben-Baruch, E., & Noy, A. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00012 · Cited by 161
An improved model-independent assessment of the late-time cosmic expansion
Haridasu, B. S., Luković, V. V., Moresco, M., & Vittorio, N. (2018) · Journal of Cosmology and Astroparticle Physics · doi:10.1088/1475-7516/2018/10/015 · Cited by 136
Multitask Learning for Cross-Domain Image Captioning
Yang, M., Zhao, W., Xu, W. H., Feng, Y., Zhao, Z., Chen, X., & Lei, K. (2018) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2018.2869276 · Cited by 134
A Multi-Task Learning Framework for Head Pose Estimation under Target Motion
Yan, Y., Ricci, E., Subramanian, R., Liu, G., Lanz, O., & Sebe, N. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2477843 · Cited by 133
Ensemble Knowledge Transfer for Semantic Segmentation
Nigam, I., Huang, C., & Ramanan, D. (2018) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2018.00168 · Cited by 139
Explainable Outfit Recommendation with Joint Outfit Matching and Comment Generation
Lin, Y., Ren, P., Chen, Z., Ren, Z., Ma, J., & de Rijke, M. (2019) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2019.2906190 · Cited by 134
Robust multi-task learning and online refinement for spacecraft pose estimation across domain gap
Park, T. H., & D’Amico, S. (2024) · Advances in Space Research · doi:10.1016/j.asr.2023.03.036 · Cited by 125
Framework for Deep Learning-Based Language Models Using Multi-Task Learning in Natural Language Understanding: A Systematic Literature Review and Future Directions
Samant, R. M., Bachute, M. R., Gite, S., & Kotecha, K. (2022) · IEEE Access · doi:10.1109/access.2022.3149798 · Cited by 123
Multi-speaker modeling and speaker adaptation for DNN-based TTS synthesis
Fan, Y., Qian, Y., Soong, F. K., & He, L. (2015) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2015.7178817 · Cited by 121
Transfer and Multi-task Learning in QSAR Modeling: Advances and Challenges
Simões, R. D. S., Maltarollo, V. G., Oliveira, P. R., & Honório, K. M. (2018) · Frontiers in Pharmacology · doi:10.3389/fphar.2018.00074 · Cited by 120
Large-Scale Modeling of Multispecies Acute Toxicity End Points Using Consensus of Multitask Deep Learning Methods
Jain, S., Siramshetty, V. B., Alves, V. M., Muratov, E. N., Kleinstreuer, N., Tropsha, A., Nicklaus, M. C., Simeonov, A. M., & Zakharov, A. V. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c01164 · Cited by 119
Graph Distillation for Action Detection with Privileged Modalities
Luo, Z., Hsieh, J.-T., Jiang, L., Niebles, J. C., & Fei-Fei, L. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01264-9_11 · Cited by 111
Blind Hyperspectral Unmixing Using Autoencoders: A Critical Comparison
Palsson, B., Sveinsson, J. R., & Ulfarsson, M. O. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3140154 · Cited by 107
Bridging the Gaps: Multi Task Learning for Domain Transfer of Hate Speech Detection
Talat, Z., Thorne, J. P., & Bingel, J. (2018) · Human-computer interaction series · doi:10.1007/978-3-319-78583-7_3 · Cited by 108
Deep Learning for Predictive Business Process Monitoring: Review and Benchmark
Rama-Maneiro, E., Vidal, J. C., & Lama, M. (2021) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2021.3139807 · Cited by 129
Transfer Learning for Predictive Models in Massive Open Online Courses
Boyer, S., & Veeramachaneni, K. K. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-19773-9_6 · Cited by 102
TopP–S: Persistent homology‐based multi‐task deep neural networks for simultaneous predictions of partition coefficient and aqueous solubility
Wu, K., Zhao, Z., Wang, R., & Wei, G. (2018) · Journal of Computational Chemistry · doi:10.1002/jcc.25213 · Cited by 101
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
Duration-Controlled LSTM for Polyphonic Sound Event Detection
Hayashi, T., Watanabe, S., Toda, T., Hori, T., Roux, J. L., & Takeda, K. (2017) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2017.2740002 · Cited by 93
AAPM task group report 273: Recommendations on best practices for AI and machine learning for computer‐aided diagnosis in medical imaging
Hadjiiski, L. M., Cha, K., Chan, H., Drukker, K., Morra, L., Näppi, J. J., Sahiner, B., Yoshida, H., Chen, Q., Deserno, T. M., et al. (2022) · Medical Physics · doi:10.1002/mp.16188 · Cited by 107
Learning From More Than One Data Source: Data Fusion Techniques for Sensorimotor Rhythm-Based Brain–Computer Interfaces
Fazli, S., Dähne, S., Samek, W., Bieszmann, F., & Müller, K. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2413993 · Cited by 90
Zenseact Open Dataset: A large-scale and diverse multimodal dataset for autonomous driving
Alibeigi, M., Ljungbergh, W., Tonderski, A., Hess, G., Lilja, A., Lindström, C., Motorniuk, D. I., Fu, J., Widahl, J., & Petersson, C. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01846 · Cited by 105
MultiNet: Multi-Modal Multi-Task Learning for Autonomous Driving
Chowdhuri, S., Pankaj, T., & Zipser, K. (2019) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2019.00164 · Cited by 82
A Survey of Multi‐task Learning Methods in Chemoinformatics
Sosnin, S., Vashurina, M., Withnall, M., Karpov, P., Fedorov, M. V., & Tetko, I. V. (2018) · Molecular Informatics · doi:10.1002/minf.201800108 · Cited by 83
A Comparison of Supervised Machine Learning Techniques for Predicting Short-Term In-Hospital Length of Stay among Diabetic Patients
Morton, A. M., Marzban, E. N., Giannoulis, G., Patel, A. K., Aparasu, R. R., & Kakadiaris, I. A. (2014) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2014.76 · Cited by 85
Real-Time Semantic Stereo Matching
Dovesi, P. L., Poggi, M., Andraghetti, L., Rabadán, M. M. I., Kjellström, H., Pieropan, A., & Mattoccia, S. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9196784 · Cited by 16
Cross-Scenario Transfer Person Reidentification
Wang, X., Zheng, W., Li, X., & Zhang, J. (2015) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2015.2450331 · Cited by 79
Transforming sensor data to the image domain for deep learning — An application to footstep detection
Singh, M. S., Pondenkandath, V., Zhou, B., Lukowicz, P., & Liwicki, M. (2017) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2017.7966182 · Cited by 82
Integrating Scene Text and Visual Appearance for Fine-Grained Image Classification
Bai, X., Yang, M., Lyu, P., Xu, Y., & Luo, J. (2018) · IEEE Access · doi:10.1109/access.2018.2878899 · Cited by 88
DAO to HANOI via DeSci: AI Paradigm Shifts from AlphaGo to ChatGPT
Miao, Q., Zheng, W., Lv, Y., Huang, M., Ding, W., & Wang, F.-Y. (2023) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2023.123561 · Cited by 78
Privacy-Preserving Distributed Multi-Task Learning with Asynchronous Updates
Xie, L., Baytaş, İ. M., Lin, K., & Zhou, J. (2017) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3097983.3098152 · Cited by 77
A Multi-Task Framework for Infrared Small Target Detection and Segmentation
Chen, Y., Li, L., Liu, X., & Su, X. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3195740 · Cited by 75
Proactive Anomaly Detection for Robot Navigation With Multi-Sensor Fusion
Ji, T., Sivakumar, A. N. V., Chowdhary, G. V., & Driggs-Campbell, K. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3153989 · Cited by 82
Multi-Modal Meta Multi-Task Learning for Social Media Rumor Detection
Zhang, H., Qian, S., Fang, Q., & Xu, C. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3065498 · Cited by 74
Imagine This! Scripts to Compositions to Videos
Gupta, T., Schwenk, D., Farhadi, A., Hoiem, D., & Kembhavi, A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01237-3_37 · Cited by 73
Multi-Task Learning for Low-Frequency Extrapolation and Elastic Model Building From Seismic Data
Ovcharenko, O., Kazei, V., Alkhalifah, T. A., & Peter, D. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3185794 · Cited by 71
Electrical Model-Free Voltage Calculations Using Neural Networks and Smart Meter Data
Bassi, V., Ochoa, L. F., Alpcan, T., & Leckie, C. A. (2022) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2022.3227602 · Cited by 69
Multi-study inference of regulatory networks for more accurate models of gene regulation
Castro, D. M., Veaux, N. D., Miraldi, E. R., & Bonneau, R. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006591 · Cited by 67
Generative Adversarial Learning for Reducing Manual Annotation in Semantic Segmentation on Large Scale Miscroscopy Images: Automated Vessel Segmentation in Retinal Fundus Image as Test Case
Lahiri, A., Ayush, K., Biswas, P. K., & Mitra, P. (2017) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.110 · Cited by 68
Investigation of transfer learning for ASR using LF-MMI trained neural networks
Ghahremani, P., Manohar, V., Hadian, H., Povey, D., & Khudanpur, S. P. (2017) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru.2017.8268947 · Cited by 66
Automatic Multi-task Learning System for Abnormal Network Traffic Detection
Huang, H., Deng, H., Chen, J., Han, L., & Wang, W. (2018) · International Journal of Emerging Technologies in Learning (iJET) · doi:10.3991/ijet.v13i04.8466 · Cited by 66
Multitask Machine Learning for Classifying Highly and Weakly Potent Kinase Inhibitors
Rodríguez-Pérez, R., & Bajorath, J. (2019) · ACS Omega · doi:10.1021/acsomega.9b00298 · Cited by 65
Explainable Identification of Dementia From Transcripts Using Transformer Networks
Ilias, L., & Askounis, D. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3172479 · Cited by 70
Deep Multitask Learning by Stacked Long Short-Term Memory for Predicting Personalized Blood Glucose Concentration
Shuvo, M. M. H., & ISLAM, S. K. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3233486 · Cited by 68
Robots for the people, by the people: Personalizing human-machine interaction
Clabaugh, C., & Matarić, M. J. (2018) · Science Robotics · doi:10.1126/scirobotics.aat7451 · Cited by 70
Distantly Supervised Lifelong Learning for Large-Scale Social Media Sentiment Analysis
Xia, R., Jiang, J., & He, H. (2017) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2017.2771234 · Cited by 65
MEL: Efficient Multi-Task Evolutionary Learning for High-Dimensional Feature Selection
Wang, X., Shangguan, H., Huang, F.-Y., Wu, S., & Jia, W. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3366333 · Cited by 62

Authors: Caruana, R.
Corresponding author: Caruana, Rich
ORCID: Caruana, Rich
Venue: Learning to Learn
Publisher: Springer US
Published: 1998
Pages: 95-133
DOI: 10.1007/978-1-4615-5529-2_5
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

Multitask Learning

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks