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Multi-task learning can leverage information learned by one task to benefit the training of other tasks.
The information complexity of learning tasks, their structure and their distance
Alessandro Achille, Giovanni Paolini, Glen Mbeng, and Stefano Soatto · 1904
Earlier work this paper cites.
Using fast weights to deblur old memories
Geoffrey E Hinton and David C Plaut · 1987
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Multitask learning: A knowledge-based source of inductive bias
Richard Caruana · 1993
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Multitask learning
Rich Caruana · 1997
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A model of inductive bias learning
Jonathan Baxter · 2000
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Exploiting task relatedness for multiple task learning
Shai Ben-David and Reba Schuller · 2003
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Convex multi-task feature learning
Andreas Argyriou, Theodoros Evgeniou, and Massimiliano Pontil · 2008
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Learning with whom to share in multi-task feature learning
Zhuoliang Kang, Kristen Grauman, and Fei Sha · 2011
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Learning task grouping and overlap in multi-task learning
Abhishek Kumar and Hal Daume III · 2012
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Accelerating stochastic gradient descent using predictive variance reduction
Rie Johnson and Tong Zhang · 2013
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Low resource dependency parsing: Cross-lingual parameter sharing in a neural network parser
Long Duong, Trevor Cohn, Steven Bird, and Paul Cook · 2015
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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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Deep neural networks for youtube recommendations
Paul Covington, Jay Adams, and Emre Sargin · 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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Asymmetric multi-task learning based on task relatedness and loss
Giwoong Lee, Eunho Yang, and Sung Hwang · 2016
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Cross-stitch networks for multi-task learning
Ishan Misra, Abhinav Shrivastava, Abhinav Gupta, and Martial Hebert · 2016
Earlier work this paper cites.
Andrei A Rusu, Neil C Rabinowitz, Guillaume Desjardins, Hubert Soyer, James Kirkpatrick, Koray Kavukcuoglu, Razvan Pascanu, and Raia Hadsell · 2016
Earlier work this paper cites.
Trace norm regularised deep multi-task learning
Yongxin Yang and Timothy M. Hospedales · 2016
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Gradnorm: Gradient normalization for adaptive loss balancing in deep multitask networks
Zhao Chen, Vijay Badrinarayanan, Chen-Yu Lee, and Andrew Rabinovich · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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Fully-adaptive feature sharing in multi-task networks with applications in person attribute classification
Yongxi Lu, Abhishek Kumar, Shuangfei Zhai, Yu Cheng, Tara Javidi, and Rogerio Feris · 2017
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An overview of multi-task learning in deep neural networks
Sebastian Ruder · 2017
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A survey on multi-task learning
Yu Zhang and Qiang Yang · 2017
Chameleon: Learning model initializations across tasks with different schemas
Lukas Brinkmeyer, Rafael Rego Drumond, Randolf Scholz, Josif Grabocka, and Lars Schmidt-Thieme · 2019
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Representation similarity analysis for efficient task taxonomy & transfer learning
Kshitij Dwivedi and Gemma Roig · 2019
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Multi-task learning in the wilderness
Andrej Karpathy · 2019
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End-to-end multi-task learning with attention
Shikun Liu, Edward Johns, and Andrew J Davison · 2019
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Which tasks should be learned together in multi-task learning?
Trevor Standley, Amir R Zamir, Dawn Chen, Leonidas Guibas, Jitendra Malik, and Silvio Savarese · 2019
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Keras: The python deep learning library
François Chollet et al · 2018
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Recasting gradient-based meta-learning as hierarchical bayes, 2018
Erin Grant, Chelsea Finn, Sergey Levine, Trevor Darrell, and Thomas Griffiths · 2018
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Dynamic task prioritization for multitask learning
Michelle Guo, Albert Haque, De-An Huang, Serena Yeung, and Li Fei-Fei · 2018
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GNAS: A greedy neural architecture search method for multi-attribute learning
Siyu Huang, Xi Li, Zhi-Qi Cheng, Zhongfei Zhang, and Alexander Hauptmann · 2018
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Averaging weights leads to wider optima and better generalization
Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry Vetrov, and Andrew Gordon Wilson · 2018
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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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Bayesian model-agnostic meta-learning
Taesup Kim, Jaesik Yoon, Ousmane Dia, Sungwoong Kim, Yoshua Bengio, and Sungjin Ahn · 2018
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Ximeng Sun, Rameswar Panda, and Rogerio Feris · 2019
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Regularizing deep multi-task networks using orthogonal gradients
Mihai Suteu and Yike Guo · 2019
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Branched multi-task networks: deciding what layers to share
Simon Vandenhende, Stamatios Georgoulis, Bert De Brabandere, and Luc Van Gool · 2019
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Lookahead optimizer: k steps forward, 1 step back
Michael Zhang, James Lucas, Jimmy Ba, and Geoffrey E Hinton · 2019
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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
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Learning to branch for multi-task learning
Pengsheng Guo, Chen-Yu Lee, and Daniel Ulbricht · 2020
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Zirui Wang, Yulia Tsvetkov, Orhan Firat, and Yuan Cao · 2020
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Understanding and improving information transfer in multi-task learning
Sen Wu, Hongyang R Zhang, and Christopher Ré · 2020
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Gradient surgery for multi-task learning
Tianhe Yu, Saurabh Kumar, Abhishek Gupta, Sergey Levine, Karol Hausman, and Chelsea Finn · 2020
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A comprehensive survey on transfer learning
Fuzhen Zhuang, Zhiyuan Qi, Keyu Duan, Dongbo Xi, Yongchun Zhu, Hengshu Zhu, Hui Xiong, and Qing He · 2020
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Mt-opt: Continuous multi-task robotic reinforcement learning at scale
Dmitry Kalashnikov, Jacob Varley, Yevgen Chebotar, Benjamin Swanson, Rico Jonschkowski, Chelsea Finn, Sergey Levine, and Karol Hausman · 2021
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Towards impartial multi-task learning
Liyang Liu, Yi Li, Zhanghui Kuang, Jing-Hao Xue, Yimin Chen, Wenming Yang, Qingmin Liao, and Wayne Zhang · 2021
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