Multitask Learning

Learning representations by back-propagating errors

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Backpropagation Applied to Handwritten Zip Code Recognition

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Multitask Learning

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Statistical Analysis With Missing Data

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Causation, Prediction, and Search

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A Bayesian Method for the Induction of Probabilistic Networks from Data

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Proceedings of the International Joint Conference on Artificial Intelligence 2007

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Hierarchical Mixtures of Experts and the EM Algorithm

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Solving Multiclass Learning Problems via Error-Correcting Output Codes

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Advances in Neural Information Processing Systems 7

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Learning distributed representations of concepts.

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Multitask Learning: A Knowledge-Based Source of Inductive Bias

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NETtalk: a parallel network that learns to read aloud

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Predicting Multivariate Responses in Multiple Linear Regression

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Neural Network Perception for Mobile Robot Guidance

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Experience with a learning personal assistant

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Using additive noise in back-propagation training

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Using Sampling and Queries to Extract Rules from Trained Neural Networks

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Learning from Incomplete Data.

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Learning from hints in neural networks

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Learning Many Related Tasks at the Same Time with Backpropagation

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An evaluation of machine-learning methods for predicting pneumonia mortality

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Learning Internal Representations

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Validation of a pneumonia prognostic index using the MedisGroups comparative hospital database

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A personal learning apprentice

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Modularity and scaling in large phonemic neural networks

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Direct transfer of learned information among neural networks

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Learning One More Thing

Thrun, S., & Mitchell, T. M. (1994) · doi:10.21236/ada285342 · Cited by 141 · see citing works

Hints

Abu‐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

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Training Neural Networks with Deficient Data

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Hints and the VC Dimension

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

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Multi-Task Learning for Stock Selection

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Rule-injection hints as a means of improving network performance and learning time

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Monotonicity Hints

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Symbolic-neural systems and the use of hints for developing complex systems

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Lifelong Learning: A Case Study.

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Conceptual Clustering, Learning from Examples, and Inference

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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

Competition among networks improves committee performance

Munro, P., & Parmanto, B. (1997) · Cited by 35 · 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:10.1007/bf00993821 · Cited by 33 · 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

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A Bayesian/information theoretic model of bias learning

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Sensor Fusion for Autonomous Outdoor Navigation using Neural Networks

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Family Discovery

Omohundro, S. M. (1995) · Cited by 16 · see citing works

A Powerful Heuristic for the Discovery of Complex Patterned Behavior

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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 3,657 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · 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

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 829 · see citing works

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 644 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

Deep neural networks in psychiatry

Durstewitz, D., Koppe, G., & Meyer‐Lindenberg, A. (2019) · Molecular Psychiatry · doi:10.1038/s41380-019-0365-9 · Cited by 287 · see citing works

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 · see citing works

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 268 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 253 · see citing works

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 243 · see citing works

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 · see citing works

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 · see citing works

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 229 · see citing works

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 · see citing works

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 223 · see citing works

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 219 · see citing works

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 · see citing works

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 · see citing works

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 210 · see citing works

Neural Approaches to Conversational AI: Question Answering, Task-oriented Dialogues and Social Chatbots

Gao, J., Galley, M., & Li, L. C. (2019) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000074 · Cited by 44 · see citing works

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 228 · see citing works

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 · see citing works

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 202 · see citing works

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 · see citing works

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 · see citing works

Transfer learning for biomedical named entity recognition with neural networks

Giorgi, J., & Bader, G. D. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty449 · Cited by 190 · see citing works

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 · see citing works

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 · see citing works

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 178 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 161 · see citing works

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 160 · see citing works

Lifelong Machine Learning

Chen, Z., & Liu, B. (2018) · Synthesis Lectures on Artificial Intelligence and Machine Learning · doi:10.2200/s00737ed1v01y201610aim033 · Cited by 154 · see citing works

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 · see citing works

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 · see citing works

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 147 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 133 · see citing works

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 126 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 116 · see citing works

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 114 · see citing works

Graph Distillation for Action Detection with Privileged Modalities

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Bridging the Gaps: Multi Task Learning for Domain Transfer of Hate Speech Detection

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Deep Learning for Predictive Business Process Monitoring: Review and Benchmark

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MultiNet: Multi-Modal Multi-Task Learning for Autonomous Driving

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A Survey of Multi‐task Learning Methods in Chemoinformatics

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A Comparison of Supervised Machine Learning Techniques for Predicting Short-Term In-Hospital Length of Stay among Diabetic Patients

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Cross-Scenario Transfer Person Reidentification

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Transforming sensor data to the image domain for deep learning — An application to footstep detection

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Integrating Scene Text and Visual Appearance for Fine-Grained Image Classification

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DAO to HANOI via DeSci: AI Paradigm Shifts from AlphaGo to ChatGPT

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Privacy-Preserving Distributed Multi-Task Learning with Asynchronous Updates

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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 · see citing works

Proactive Anomaly Detection for Robot Navigation With Multi-Sensor Fusion

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Multi-Modal Meta Multi-Task Learning for Social Media Rumor Detection

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Imagine This! Scripts to Compositions to Videos

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Multi-Task Learning for Low-Frequency Extrapolation and Elastic Model Building From Seismic Data

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Electrical Model-Free Voltage Calculations Using Neural Networks and Smart Meter Data

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Multi-study inference of regulatory networks for more accurate models of gene regulation

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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

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Investigation of transfer learning for ASR using LF-MMI trained neural networks

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Automatic Multi-task Learning System for Abnormal Network Traffic Detection

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Multitask Machine Learning for Classifying Highly and Weakly Potent Kinase Inhibitors

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Explainable Identification of Dementia From Transcripts Using Transformer Networks

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Deep Multitask Learning by Stacked Long Short-Term Memory for Predicting Personalized Blood Glucose Concentration

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Robots for the people, by the people: Personalizing human-machine interaction

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Distantly Supervised Lifelong Learning for Large-Scale Social Media Sentiment Analysis

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MEL: Efficient Multi-Task Evolutionary Learning for High-Dimensional Feature Selection

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