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Multi-task learning (MTL) is a subfield of machine learning with important applications, but the multi-objective nature of optimization in MTL leads to difficulties in balancing training between tasks.
Multitask learning
Rich Caruana · 1997
Earlier work this paper cites.
Rdkit: Open-source cheminformatics, 2006
Greg Landrum · 2006
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A unified architecture for natural language processing: Deep neural networks with multitask learning
Ronan Collobert and Jason Weston · 2008
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Virtual screening for the discovery of bioactive natural products
Judith M. Rollinger, Hermann Stuppner, and Thierry Langer · 2008
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Extended-connectivity fingerprints
David Rogers and Mathew Hahn · 2010
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Indoor segmentation and support inference from rgbd images
Pushmeet Kohli Nathan Silberman, Derek Hoiem and Rob Fergus · 2012
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David Eigen, Christian Puhrsch, and Rob Fergus · 2014
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Facial landmark detection by deep multi-task learning
Zhanpeng Zhang, Ping Luo, Chen Change Loy, and Xiaoou Tang · 2014
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Instance-aware semantic segmentation via multi-task network cascades, 2015
Jifeng Dai, Kaiming He, and Jian Sun · 2015
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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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Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture, 2015
David Eigen and Rob Fergus · 2015
Earlier work this paper cites.
Actor-mimic: Deep multitask and transfer reinforcement learning
Emilio Parisotto, Jimmy Lei Ba, and Ruslan Salakhutdinov · 2015
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Massively multitask networks for drug discovery
Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, and Vijay Pande · 2015
Earlier work this paper cites.
Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
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When is multitask learning effective? semantic sequence prediction under varying data conditions
Héctor Martínez Alonso and Barbara Plank · 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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Ishan Misra, Abhinav Shrivastava, Abhinav Gupta, and Martial Hebert · 2016
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Yanli Wang, Stephen H. Bryant, Tiejun Cheng, Jiyao Wang, Asta Gindulyte, Benjamin A. Shoemaker, Paul A. Thiessen, Siqian He, and Jian Zhang · 2016
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Taskonomy: Disentangling task transfer learning
Amir R Zamir, Alexander Sax, William Shen, Leonidas J Guibas, Jitendra Malik, and Silvio Savarese · 2018
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Pyramidal person re-identification via multi-loss dynamic training, 2018
Feng Zheng, Cheng Deng, Xing Sun, Xinyang Jiang, Xiaowei Guo, Zongqiao Yu, Feiyue Huang, and Rongrong Ji · 2018
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Task2vec: Task embedding for meta-learning
Alessandro Achille, Michael Lam, Rahul Tewari, Avinash Ravichandran, Subhransu Maji, Charless C Fowlkes, Stefano Soatto, and Pietro Perona · 2019
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Nddr-cnn: Layerwise feature fusing in multi-task cnns by neural discriminative dimensionality reduction
Yuan Gao, Jiayi Ma, Mingbo Zhao, Wei Liu, and Alan L Yuille · 2019
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End-to-end multi-task learning with attention
S. Liu, E. Johns, and A. J. Davison · 2019
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Zhao Chen, Vijay Badrinarayanan, Chen-Yu Lee, and Andrew Rabinovich · 2017
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Multi-task learning using uncertainty to weigh losses for scene geometry and semantics, 2017
Alex Kendall, Yarin Gal, and Roberto Cipolla · 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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Deep learning for healthcare: review, opportunities and challenges
Riccardo Miotto, Fei Wang, Shuang Wang, Xiaoqian Jiang, and Joel T Dudley · 2017
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Learning to push by grasping: Using multiple tasks for effective learning
Lerrel Pinto and Abhinav Gupta · 2017
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A survey on multi-task learning
Yu Zhang and Qiang Yang · 2017
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Efficient lifelong learning with a-gem
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Deep Learning for the Life Sciences
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Latent multi-task architecture learning
Sebastian Ruder, Joachim Bingel, Isabelle Augenstein, and Anders Søgaard · 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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Multi-task learning with deep neural networks: A survey, 2020
Michael Crawshaw · 2020
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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 · 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 continual learning survey: Defying forgetting in classification tasks
Matthias Delange, Rahaf Aljundi, Marc Masana, Sarah Parisot, Xu Jia, Ales Leonardis, Greg Slabaugh, and Tinne Tuytelaars · 2021
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Meta-learning in neural networks: A survey
Timothy M Hospedales, Antreas Antoniou, Paul Micaelli, and Amos J Storkey · 2021
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