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Multi-task learning aims to improve generalization performance of multiple prediction tasks by appropriately sharing relevant information across them.
Multi-task learning
R. Caruana · 1997
Earlier work this paper cites.
Learning to learn
S. Thrun and L. Pratt · 1998
Earlier work this paper cites.
Algorithms for simultaneous sparse approximation. part ii: Convex relaxation
J. A. Tropp · 2006
Earlier work this paper cites.
Algorithms for simultaneous sparse approximation. part i: Greedy pursuit
J. A. Tropp, A. C. Gilbert, and M. J. Strauss · 2006
Earlier work this paper cites.
Multi-task learning for classification with dirichlet process priors
Y. Xue, X. Liao, L. Carin, and B. Krishnapuram · 2007
Earlier work this paper cites.
Clustered multi-task learning: A convex formulation
L. Jacob, J.-p. Vert, and F. R. Bach · 2009
Earlier work this paper cites.
Attribute-based people search in surveillance environments
D. A. Vaquero, R. S. Feris, D. Tran, L. Brown, A. Hampapur, and M. Turk · 2009
Earlier work this paper cites.
Learning with whom to share in multi-task feature learning
Z. Kang, K. Grauman, and F. Sha · 2011
Earlier work this paper cites.
Clustered multi-task learning via alternating structure optimization
J. Zhou, J. Chen, and J. Ye · 2011
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
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Earlier work this paper cites.
Learning task grouping and overlap in multi-task
A. Kumar and H. Daume III · 2012
Earlier work this paper cites.
Flexible modeling of latent task structures in multitask learning
A. Passos, P. Rai, J. Wainer, and H. Daume III · 2012
Earlier work this paper cites.
Low-rank matrix factorization for deep neural network training with high-dimensional output targets
T. N. Sainath, B. Kingsbury, V. Sindhwani, E. Arisoy, and B. Ramabhadran · 2013
Earlier work this paper cites.
Exploiting linear structure within convolutional networks for efficient evaluation
E. L. Denton, W. Zaremba, J. Bruna, Y. LeCun, and R. Fergus · 2014
Earlier work this paper cites.
Attribute-based people search: Lessons learnt from a practical surveillance system
R. Feris, R. Bobbitt, L. Brown, and S. Pankanti · 2014
Earlier work this paper cites.
Wow! you are so beautiful today!
L. Liu, J. Xing, S. Liu, H. Xu, X. Zhou, and S. Yan · 2014
Earlier work this paper cites.
Fitnets: Hints for thin deep nets
A. Romero, N. Ballas, S. E. Kahou, A. Chassang, C. Gatta, and Y. Bengio · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
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Visualizing and understanding convolutional networks
M. D. Zeiler and R. Fergus · 2014
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Q. Chen, J. Huang, R. Feris, L. M. Brown, J. Dong, and S. Yan · 2015
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Y. Cheng, F. Yu, R. Feris, S. Kumar, A. Choudhary, and S. F. Chang · 2015
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Integrated perception with recurrent multi-task neural networks
H. Bilen and A. Vedaldi · 2016
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Training cnns with low-rank filters for efficient image classification
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Deep cross residual learning for multi-task visual recognition
B. Jou and S. F. Chang · 2016
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Cross-domain image retrieval with a dual attribute-aware ranking network
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
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