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Multi-task learning aims to learn multiple related tasks simultaneously and has achieved great success in various fields.
Multitask learning: A knowledge-based source of inductive bias
Rich Caruana · 1993
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Multitask learning
Rich Caruana · 1997
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Convex optimization
Stephen Boyd and Lieven Vandenberghe · 2004
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ImageNet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Adapting visual category models to new domains
Kate Saenko, Brian Kulis, Mario Fritz, and Trevor Darrell · 2010
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Multiple-gradient descent algorithm (MGDA) for multiobjective optimization
Jean-Antoine Désidéri · 2012
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Indoor segmentation and support inference from RGBD images
Nathan Silberman, Derek Hoiem, Pushmeet Kohli, and Rob Fergus · 2012
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RMSProp: Neural networks for machine learning
Tijmen Tieleman and Geoffrey Hinton · 2012
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AdaDelta: an adaptive learning rate method
Matthew D Zeiler · 2012
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Depth map prediction from a single image using a multi-scale deep network
David Eigen, Christian Puhrsch, and Rob Fergus · 2014
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Quantum chemistry structures and properties of 134 kilo molecules
Raghunathan Ramakrishnan, Pavlo O Dral, Matthias Rupp, and O Anatole Von Lilienfeld · 2014
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Fast R-CNN
Ross Girshick · 2015
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
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The cityscapes dataset for semantic urban scene understanding
Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Cross-stitch networks for multi-task learning
Ishan Misra, Abhinav Shrivastava, Abhinav Gupta, and Martial Hebert · 2016
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SegNet: A deep convolutional encoder-decoder architecture for image segmentation
Vijay Badrinarayanan, Alex Kendall, and Roberto Cipolla · 2017
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
Cited alongside, same era.
Adversarial multi-task learning for text classification
Pengfei Liu, Xipeng Qiu, and Xuan-Jing Huang · 2017
Cited alongside, same era.
Deep hashing network for unsupervised domain adaptation
Hemanth Venkateswara, Jose Eusebio, Shayok Chakraborty, and Sethuraman Panchanathan · 2017
Cited alongside, same era.
Multi-task learning using uncertainty to weigh losses for scene geometry and semantics
Alex Kendall, Yarin Gal, and Roberto Cipolla · 2018
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Multi-task learning as multi-objective optimization
Ozan Sener and Vladlen Koltun · 2018
Cited alongside, same era.
MultiNet++: Multi-stream feature aggregation and geometric loss strategy for multi-task learning
Sumanth Chennupati, Ganesh Sistu, Senthil Yogamani, and Samir A Rawashdeh · 2019
Dselect-k: Differentiable selection in the mixture of experts with applications to multi-task learning
Hussein Hazimeh, Zhe Zhao, Aakanksha Chowdhery, Maheswaran Sathiamoorthy, Yihua Chen, Rahul Mazumder, Lichan Hong, and Ed Chi · 2021
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MTAdam: Automatic balancing of multiple training loss terms
Itzik Malkiel and Lior Wolf · 2021
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Multi-task learning for dense prediction tasks: A survey
Simon Vandenhende, Stamatios Georgoulis, Wouter Van Gansbeke, Marc Proesmans, Dengxin Dai, and Luc Van Gool · 2021
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Gradient vaccine: Investigating and improving multi-task optimization in massively multilingual models
Zirui Wang, Yulia Tsvetkov, Orhan Firat, and Yuan Cao · 2021
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Multi-objective meta learning
Feiyang Ye, Baijiong Lin, Zhixiong Yue, Pengxin Guo, Qiao Xiao, and Yu Zhang · 2021
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Cited alongside, same era.
Fast graph representation learning with pytorch geometric
Matthias Fey and Jan Eric Lenssen · 2019
Cited alongside, same era.
Attentive single-tasking of multiple tasks
Kevis-Kokitsi Maninis, Ilija Radosavovic, and Iasonas Kokkinos · 2019
Cited alongside, same era.
PyTorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Yang, Zach DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
Cited alongside, same era.
Just pick a sign: Optimizing deep multitask models with gradient sign dropout
Zhao Chen, Jiquan Ngiam, Yanping Huang, Thang Luong, Henrik Kretzschmar, Yuning Chai, and Dragomir Anguelov · 2020
Cited alongside, same era.
Directional message passing for molecular graphs
Johannes Gasteiger, Janek Groß, and Stephan Günnemann · 2020
Cited alongside, same era.
Which tasks should be learned together in multi-task learning?
Trevor Standley, Amir Zamir, Dawn Chen, Leonidas Guibas, Jitendra Malik, and Silvio Savarese · 2020
Cited alongside, same era.
MetaBalance: improving multi-task recommendations via adapting gradient magnitudes of auxiliary tasks
Yun He, Xue Feng, Cheng Cheng, Geng Ji, Yunsong Guo, and James Caverlee · 2022
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In defense of the unitary scalarization for deep multi-task learning
Vitaly Kurin, Alessandro De Palma, Ilya Kostrikov, Shimon Whiteson, and M Pawan Kumar · 2022
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Reasonable effectiveness of random weighting: A litmus test for multi-task learning
Baijiong Lin, Feiyang Ye, Yu Zhang, and Ivor Tsang · 2022
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Multi-task learning as a bargaining game
Aviv Navon, Aviv Shamsian, Idan Achituve, Haggai Maron, Kenji Kawaguchi, Gal Chechik, and Ethan Fetaya · 2022
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Do current multi-task optimization methods in deep learning even help?
Derrick Xin, Behrooz Ghorbani, Justin Gilmer, Ankush Garg, and Orhan Firat · 2022
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MTFormer: Multi-task learning via transformer and cross-task reasoning
Xiaogang Xu, Hengshuang Zhao, Vibhav Vineet, Ser-Nam Lim, and Antonio Torralba · 2022
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Inverted pyramid multi-task transformer for dense scene understanding
Hanrong Ye and Dan Xu · 2022
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A survey on multi-task learning
Yu Zhang and Qiang Yang · 2022
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Mitigating gradient bias in multi-objective learning: A provably convergent approach
Heshan Devaka Fernando, Han Shen, Miao Liu, Subhajit Chaudhury, Keerthiram Murugesan, and Tianyi Chen · 2023
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LibMTL: A Python library for multi-task learning
Baijiong Lin and Yu Zhang · 2023
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Independent component alignment for multi-task learning
Dmitry Senushkin, Nikolay Patakin, Arseny Kuznetsov, and Anton Konushin · 2023
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Multi-task deep recommender systems: A survey
Yuhao Wang, Ha Tsz Lam, Yi Wong, Ziru Liu, Xiangyu Zhao, Yichao Wang, Bo Chen, Huifeng Guo, and Ruiming Tang · 2023
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