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Multi-task learning holds the promise of less data, parameters, and time than training of separate models.
Multitask learning
R. Caruana · 1997
Earlier work this paper cites.
Catastrophic forgetting in connectionist networks
R. M. French · 1999
Earlier work this paper cites.
Natural evolution strategies
D. Wierstra, T. Schaul, J. Peters, and J. Schmidhuber · 2008
Earlier work this paper cites.
A. A. Rusu, S. G. Colmenarejo, C. Gulcehre, G. Desjardins, J. Kirkpatrick, R. Pascanu, V. Mnih, K. Kavukcuoglu, and R. Hadsell · 2015
Earlier work this paper cites.
Cross-stitch networks for multi-task learning
I. Misra, A. Shrivastava, A. Gupta, and M. Hebert · 2016
Earlier work this paper cites.
Neural architecture search with reinforcement learning
B. Zoph and Q. V. Le · 2016
Earlier work this paper cites.
Accelerating neural architecture search using performance prediction
B. Baker, O. Gupta, R. Raskar, and N. Naik · 2017
Earlier work this paper cites.
Smash: one-shot model architecture search through hypernetworks
A. Brock, T. Lim, J. M. Ritchie, and N. Weston · 2017
Earlier work this paper cites.
Peephole: Predicting network performance before training
B. Deng, J. Yan, and D. Lin · 2017
Earlier work this paper cites.
Pathnet: Evolution channels gradient descent in super neural networks
C. Fernando, D. Banarse, C. Blundell, Y. Zwols, D. Ha, A. A. Rusu, A. Pritzel, and D. Wierstra · 2017
Earlier work this paper cites.
L. Kaiser, A. N. Gomez, N. Shazeer, A. Vaswani, N. Parmar, L. Jones, and J. Uszkoreit · 2017
Earlier work this paper cites.
Progressive neural architecture search
C. Liu, B. Zoph, J. Shlens, W. Hua, L.-J. Li, L. Fei-Fei, A. Yuille, J. Huang, and K. Murphy · 2017
Earlier work this paper cites.
Hierarchical representations for efficient architecture search
H. Liu, K. Simonyan, O. Vinyals, C. Fernando, and K. Kavukcuoglu · 2017
Earlier work this paper cites.
Beyond shared hierarchies: Deep multitask learning through soft layer ordering
E. Meyerson and R. Miikkulainen · 2017
Cited alongside, same era.
Learning multiple visual domains with residual adapters
S.-A. Rebuffi, H. Bilen, and A. Vedaldi · 2017
Cited alongside, same era.
Routing networks: Adaptive selection of non-linear functions for multi-task learning
C. Rosenbaum, T. Klinger, and M. Riemer · 2017
Cited alongside, same era.
Incremental learning through deep adaptation
A. Rosenfeld and J. K. Tsotsos · 2017
Cited alongside, same era.
An overview of multi-task learning in deep neural networks
S. Ruder · 2017
Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels
L. Jiang, Z. Zhou, T. Leung, L.-J. Li, and L. Fei-Fei · 2018
Later among the works it cites.
Parallel architecture and hyperparameter search via successive halving and classification
M. Kumar, G. E. Dahl, V. Vasudevan, and M. Norouzi · 2018
Later among the works it cites.
Evolutionary architecture search for deep multitask networks
J. Liang, E. Meyerson, and R. Miikkulainen · 2018
Later among the works it cites.
End-to-end multi-task learning with attention
S. Liu, E. Johns, and A. J. Davison · 2018
Later among the works it cites.
Piggyback: Adding multiple tasks to a single, fixed network by learning to mask
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Learning what to share between loosely related tasks
S. Ruder, J. Bingel, I. Augenstein, and A. Søgaard · 2017
Cited alongside, same era.
Learning to multi-task by active sampling
S. Sharma, A. Jha, P. Hegde, and B. Ravindran · 2017
Cited alongside, same era.
A gift from knowledge distillation: Fast optimization, network minimization and transfer learning
J. Yim, D. Joo, J. Bae, and J. Kim · 2017
Cited alongside, same era.
A survey on multi-task learning
Y. Zhang and Q. Yang · 2017
Cited alongside, same era.
Combating catastrophic forgetting with developmental compression
S. L. Beaulieu, S. Kriegman, and J. C. Bongard · 2018
Cited alongside, same era.
Unifying and merging well-trained deep neural networks for inference stage
Y.-M. Chou, Y.-M. Chan, J.-H. Lee, C.-Y. Chiu, and C.-S. Chen · 2018
Cited alongside, same era.
Impala: Scalable distributed deep-rl with importance weighted actor-learner architectures
L. Espeholt, H. Soyer, R. Munos, K. Simonyan, V. Mnih, T. Ward, Y. Doron, V. Firoiu, T. Harley, I. Dunning, et al · 2018
Cited alongside, same era.
A. Mallya and S. Lazebnik · 2018
Later among the works it cites.
Simple random search provides a competitive approach to reinforcement learning
H. Mania, A. Guy, and B. Recht · 2018
Later among the works it cites.
Efficient neural architecture search via parameter sharing
H. Pham, M. Y. Guan, B. Zoph, Q. V. Le, and J. Dean · 2018
Later among the works it cites.
Regularized evolution for image classifier architecture search
E. Real, A. Aggarwal, Y. Huang, and Q. V. Le · 2018
Later among the works it cites.
Efficient parametrization of multi-domain deep neural networks
S.-A. Rebuffi, H. Bilen, and A. Vedaldi · 2018
Later among the works it cites.
Lifelong learning with dynamically expandable networks
J. Yoon, E. Yang, J. Lee, and S. J. Hwang · 2018
Later among the works it cites.
Taskonomy: Disentangling task transfer learning
A. R. Zamir, A. Sax, W. Shen, L. Guibas, J. Malik, and S. Savarese · 2018
Later among the works it cites.
Towards automated deep learning: Efficient joint neural architecture and hyperparameter search
A. Zela, A. Klein, S. Falkner, and F. Hutter · 2018
Later among the works it cites.
Random search and reproducibility for neural architecture search
L. Li and A. Talwalkar · 2019
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