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Tasks in multi-task learning often correlate, conflict, or even compete with each other.
Fast exact multiplication by the hessian
Pearlmutter, B. A · 1994
Earlier work this paper cites.
Scaling up the accuracy of naive-bayes classifiers: A decision-tree hybrid
Kohavi, R · 1996
Earlier work this paper cites.
Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
Earlier work this paper cites.
Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach
Zitzler, E. and Thiele, L · 1999
Earlier work this paper cites.
Steepest descent methods for multicriteria optimization
Fliege, J. and Svaiter, B. F · 2000
Earlier work this paper cites.
Generalized homotopy approach to multiobjective optimization
Hillermeier, C · 2001
Earlier work this paper cites.
Nonlinear multiobjective optimization: a generalized homotopy approach , volume 135
Hillermeier, C. et al · 2001
Earlier work this paper cites.
Comparison of multiobjective evolutionary algorithms: Empirical results
Zitzler, E., Deb, K., and Thiele, L · 2006
Earlier work this paper cites.
Deep learning via hessian-free optimization
Martens, J · 2010
Earlier work this paper cites.
MINRES-QLP: A Krylov subspace method for indefinite or singular symmetric systems
Choi, S.-C. T., Paige, C. C., and Saunders, M. A · 2011
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Multiple-gradient descent algorithm (MGDA) for multiobjective optimization
Désidéri, J.-A · 2012
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CG versus MINRES: An empirical comparison
Fong, D. C.-L. and Saunders, M · 2012
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Krylov subspace descent for deep learning
Vinyals, O. and Povey, D · 2012
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A method for constrained multiobjective optimization based on SQP techniques
Fliege, J. and Vaz, A. I. F · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms
Xiao, H., Rasul, K., and Vollgraf, R · 2017
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Age progression/regression by conditional adversarial autoencoder
Zhang, Z., Song, Y., and Qi, H · 2017
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Gradnorm: Gradient normalization for adaptive loss balancing in deep multitask networks
Chen, Z., Badrinarayanan, V., Lee, C.-Y., and Rabinovich, A · 2018
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Multi-task learning using uncertainty to weigh losses for scene geometry and semantics
Kendall, A., Gal, Y., and Cipolla, R · 2018
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Pareto tracer: a predictor–corrector method for multi-objective optimization problems
Martín, A. and Schütze, O · 2018
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Cross-stitch networks for multi-task learning
Misra, I., Shrivastava, A., Gupta, A., and Hebert, M · 2016
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Ubernet: Training a universal convolutional neural network for low-, mid-, and high-level vision using diverse datasets and limited memory
Kokkinos, I · 2017
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Dynamic routing between capsules
Sabour, S., Frosst, N., and Hinton, G. E · 2017
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Schulz, A., Wang, H., Grinspun, E., Solomon, J., and Matusik, W · 2018
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Multi-task learning as multi-objective optimization
Sener, O. and Koltun, V · 2018
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Pareto multi-task learning
Lin, X., Zhen, H.-L., Li, Z., Zhang, Q.-F., and Kwong, S · 2019
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