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Multi-task learning (MTL) models have demonstrated impressive results in computer vision, natural language processing, and recommender systems.
Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts
Ma, J.; Zhao, Z.; Yi, X.; Chen, J.; Hong, L.; and Chi, E. H. 2018a · 1939
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Multitask learning
Caruana, R. 1997 · 1997
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On the momentum term in gradient descent learning algorithms
Qian, N. 1999 · 1999
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An introduction to ROC analysis
Fawcett, T. 2006 · 2006
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Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
Duchi, J. C.; Hazan, E.; and Singer, Y. 2011 · 2011
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ADADELTA: An Adaptive Learning Rate Method
Zeiler, M. D. 2012 · 2012
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How transferable are features in deep neural networks?
Yosinski, J.; Clune, J.; Bengio, Y.; and Lipson, H. 2014 · 2014
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Multi-task learning for multiple language translation
Dong, D.; Wu, H.; He, W.; Yu, D.; and Wang, H. 2015 · 2015
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Adam: A Method for Stochastic Optimization
Kingma, D. P.; and Ba, J. 2015 · 2015
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Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
Clevert, D.; Unterthiner, T.; and Hochreiter, S. 2016 · 2016
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Cross-Stitch Networks for Multi-task Learning
Misra, I.; Shrivastava, A.; Gupta, A.; and Hebert, M. 2016 · 2016
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Segnet: A deep convolutional encoder-decoder architecture for image segmentation
Badrinarayanan, V.; Kendall, A.; and Cipolla, R. 2017 · 2017
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Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
Shazeer, N.; Mirhoseini, A.; Maziarz, K.; Davis, A.; Le, Q. V.; Hinton, G. E.; and Dean, J. 2017 · 2017
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Gradnorm: Gradient normalization for adaptive loss balancing in deep multitask networks
Chen, Z.; Badrinarayanan, V.; Lee, C.-Y.; and Rabinovich, A. 2018 · 2018
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Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
Kendall, A.; Gal, Y.; and Cipolla, R. 2018 · 2018
Cited alongside, same era.
On the Convergence of Adam and Beyond
Reddi, S. J.; Kale, S.; and Kumar, S. 2018 · 2018
Cited alongside, same era.
Multi-Task Learning as Multi-Objective Optimization
Sener, O.; and Koltun, V. 2018 · 2018
Cited alongside, same era.
Taskonomy: Disentangling task transfer learning
Zamir, A. R.; Sax, A.; Shen, W.; Guibas, L. J.; Malik, J.; and Savarese, S. 2018 · 2018
Cited alongside, same era.
Weighted adagrad with unified momentum
Zou, F.; Shen, L.; Jie, Z.; Sun, J.; and Liu, W. 2018 · 2018
Cited alongside, same era.
End-To-End Multi-Task Learning With Attention
Liu, S.; Johns, E.; and Davison, A. J. 2019 · 2019
Adaptive activation network and functional regularization for efficient and flexible deep multi-task learning
Liu, Y.; Yang, X.; Xie, D.; Wang, X.; Shen, L.; Huang, H.; and Balasubramanian, N. 2020 · 2020
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Efficient continuous pareto exploration in multi-task learning
Ma, P.; Du, T.; and Matusik, W. 2020 · 2020
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Adashare: Learning what to share for efficient deep multi-task learning
Sun, X.; Panda, R.; Feris, R.; and Saenko, K. 2020 · 2020
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Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations
Tang, H.; Liu, J.; Zhao, M.; and Gong, X. 2020 · 2020
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Gradient Surgery for Multi-Task Learning
Yu, T.; Kumar, S.; Gupta, A.; Levine, S.; Hausman, K.; and Finn, C. 2020 · 2020
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Cited alongside, same era.
Loss-Balanced Task Weighting to Reduce Negative Transfer in Multi-Task Learning
Liu, S.; Liang, Y.; and Gitter, A. 2019 · 2019
Cited alongside, same era.
Predicting different types of conversions with multi-task learning in online advertising
Pan, J.; Mao, Y.; Ruiz, A. L.; Sun, Y.; and Flores, A. 2019 · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Radford, A.; Wu, J.; Child, R.; Luan, D.; Amodei, D.; Sutskever, I.; et al. 2019 · 2019
Cited alongside, same era.
Latent Multi-Task Architecture Learning
Ruder, S.; Bingel, J.; Augenstein, I.; and Søgaard, A. 2019 · 2019
Cited alongside, same era.
Multiple relational attention network for multi-task learning
Zhao, J.; Du, B.; Sun, L.; Zhuang, F.; Lv, W.; and Xiong, H. 2019 · 2019
Cited alongside, same era.
A sufficient condition for convergences of adam and rmsprop
Zou, F.; Shen, L.; Jie, Z.; Zhang, W.; and Liu, W. 2019 · 2019
Cited alongside, same era.
Zhuang, J.; Tang, T.; Ding, Y.; Tatikonda, S. C.; Dvornek, N.; Papademetris, X.; and Duncan, J. 2020 · 2020
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Quantized adam with error feedback
Chen, C.; Shen, L.; Huang, H.; and Liu, W. 2021 · 2021
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Conflict-Averse Gradient Descent for Multi-task Learning
Liu, B.; Liu, X.; Jin, X.; Stone, P.; and Liu, Q. 2021 · 2021
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Auxiliary Learning by Implicit Differentiation
Navon, A.; Achituve, I.; Maron, H.; Chechik, G.; and Fetaya, E. 2021 · 2021
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Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertising
Xi, D.; Chen, Z.; Yan, P.; Zhang, Y.; Zhu, Y.; Zhuang, F.; and Chen, Y. 2021 · 2021
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Personalized Approximate Pareto-Efficient Recommendation
Xie, R.; Liu, Y.; Zhang, S.; Wang, R.; Xia, F.; and Lin, L. 2021 · 2021
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MetaBalance: Improving Multi-Task Recommendations via Adapting Gradient Magnitudes of Auxiliary Tasks
He, Y.; Feng, X.; Cheng, C.; Ji, G.; Guo, Y.; and Caverlee, J. 2022 · 2022
Closest in time.
Cross-Task Knowledge Distillation in Multi-Task Recommendation
Yang, C.; Pan, J.; Gao, X.; Jiang, T.; Liu, D.; and Chen, G. 2022 · 2022
Closest in time.