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Deep neural networks are a promising approach towards multi-task learning because of their capability to leverage knowledge across domains and learn general purpose representations.
On the variance of the adaptive learning rate and beyond
Liyuan Liu, Haoming Jiang, Pengcheng He, Weizhu Chen, Xiaodong Liu, Jianfeng Gao, and Jiawei Han · 1908
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Double backpropagation increasing generalization performance
Harris Drucker and Yann Le Cun · 1991
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Using semi-distributed representations to overcome catastrophic forgetting in connectionist networks
Robert M French · 1991
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Multitask learning
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Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Adam: A method for stochastic optimization
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Very deep convolutional networks for large-scale image recognition
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Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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How transferable are features in deep neural networks?
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Adapting auxiliary losses using gradient similarity
Yunshu Du, Wojciech M Czarnecki, Siddhant M Jayakumar, Razvan Pascanu, and Balaji Lakshminarayanan · 2018
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Multi-task learning using uncertainty to weigh losses for scene geometry and semantics
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Learning to learn without forgetting by maximizing transfer and minimizing interference
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How does batch normalization help optimization?
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Taskonomy: Disentangling task transfer learning
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A modulation module for multi-task learning with applications in image retrieval
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Meta-learning representations for continual learning
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Which tasks should be learned together in multi-task learning?
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Multi-digit mnist for few-shot learning, 2019
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