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A multi-task learning (MTL) system aims at solving multiple related tasks at the same time.
Multi-task learning for dense prediction tasks: A survey
Vandenhende, S., Georgoulis, S., Van Gansbeke, W., Proesmans, M., Dai, D., and Van Gool, L · 1939
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Learning to control fast-weight memories: an alternative to dynamic recurrent networks
Schmidhuber, J · 1992
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Multitask learning
Caruana, R · 1997
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A closer look at drawbacks of minimizing weighted sums of objectives for pareto set generation in multicriteria optimization problems
Das, I. and Dennis, J · 1997
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Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach
Zitzler, E. and Thiele, L · 1999
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Steepest descent methods for multicriteria optimization
Fliege, J. and Svaiter, B. F · 2000
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Convex optimization
Boyd, S. and Vandenberghe, L · 2004
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Moea/d: A multiobjective evolutionary algorithm based on decomposition
Zhang, Q. and Li, H · 2007
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Learning multiple layers of features from tiny images
Krizhevsky, A. and Hinton, G · 2009
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Mutiple-gradient descent algorithm for multiobjective optimization
Désidéri, J.-A · 2012
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Nonlinear multiobjective optimization , volume 12
Miettinen, K · 2012
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Indoor segmentation and support inference from rgbd images
Silberman, N., Hoiem, D., Kohli, P., and Fergus, R · 2012
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Revisiting frank-wolfe: Projection-free sparse convex optimization
Jaggi, M · 2013
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Decomposition of a multiobjective optimization problem into a number of simple multiobjective subproblems
Liu, H.-L., Gu, F., and Zhang, Q · 2014
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Multi-objective reinforcement learning using sets of pareto dominating policies
Van Moffaert, K. and Nowé, A · 2014
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The cityscapes dataset for semantic urban scene understanding
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., and Schiele, B · 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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Cross-stitch networks for multi-task learning
Misra, I., Shrivastava, A., Gupta, A., and Hebert, M · 2016
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Multi-objective reinforcement learning through continuous pareto manifold approximation
Parisi, S., Pirotta, M., and Restelli, M · 2016
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Segnet: A deep convolutional encoder-decoder architecture for image segmentation
Badrinarayanan, V., Kendall, A., and Cipolla, R · 2017
Cited alongside, same era.
A descent method for equality and inequality constrained multiobjective optimization problems
Gebken, B., Peitz, S., and Dellnitz, M · 2017
Cited alongside, same era.
Hypernetworks
Ha, D., Dai, A. M., and Le, Q. V · 2017
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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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Krueger, D., Huang, C.-W., Islam, R., Turner, R., Lacoste, A., and Courville, A · 2017
Cited alongside, same era.
Pareto multi-task learning
Lin, X., Zhen, H., Li, Z., Zhang, Q., and Kwong, S · 2019
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End-to-end multi-task learning with attention
Liu, S., Johns, E., and Davison, A. J · 2019
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Self-tuning networks: Bilevel optimization of hyperparameters using structured best-response functions
MacKay, M., Vicol, P., Lorraine, J., Duvenaud, D., and Grosse, R · 2019
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Modular universal reparameterization: Deep multi-task learning across diverse domains
Meyerson, E. and Miikkulainen, R · 2019
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A generalized algorithm for multi-objective reinforcement learning and policy adaptation
Yang, R., Sun, X., and Narasimhan, K · 2019
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Principled weight initialization for hypernetworks
Chang, O., Flokas, L., and Lipson, H · 2020
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Long, M., Cao, Z., Wang, J., and Yu, P. S · 2017
Cited alongside, same era.
An overview of multi-task learning in deep neural networks
Ruder, S · 2017
Cited alongside, same era.
Dynamic routing between capsules
Sabour, S., Frosst, N., and Hinton, G. E · 2017
Cited alongside, same era.
Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Xiao, H., Rasul, K., and Vollgraf, R · 2017
Cited alongside, same era.
Deep multi-task representation learning: A tensor factorisation approach
Yang, Y. and Hospedales, T. M · 2017
Cited alongside, same era.
A survey on multi-task learning
Zhang, Y. and Yang, Q · 2017
Cited alongside, same era.
Smash: One-shot model architecture search through hypernetworks
Brock, A., Lim, T., Ritchie, J., and Weston, N · 2018
Cited alongside, same era.
Closest in time.
You only train once: Loss-conditional training of deep networks
Dosovitskiy, A. and Djolonga, J · 2020
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Hypermodels for exploration
Dwaracherla, V., Lu, X., Ibrahimi, M., Osband, I., Wen, Z., and Roy, B. V · 2020
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Multiplicative interactions and where to find them
Jayakumar, S. M., Menick, J., Czarnecki, W. M., Schwarz, J., Rae, J., Osindero, S., Teh, Y. W., Harley, T., and Pascanu, R · 2020
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On the optimization dynamics of wide hypernetworks
Littwin, E., Galanti, T., and Wolf, L · 2020
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Lu, Z., Sreekumar, G., Goodman, E., Banzhaf, W., Deb, K., and Boddeti, V. N · 2020
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Efficient continuous pareto exploration in multi-task learning
Ma, P., Du, T., and Matusik, W · 2020
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Multi-task learning with user preferences: Gradient descent with controlled ascent in pareto optimization
Mahapatra, D. and Rajan, V · 2020
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Multi-gradient descent for multi-objective recommender systems
Milojkovic, N., Antognini, D., Bergamin, G., Faltings, B., and Musat, C · 2020
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Which tasks should be learned together in multi-task learning?
Standley, T., Zamir, A. R., Chen, D., Guibas, L. J., Malik, J., and Savarese, S · 2020
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Continual learning with hypernetworks
von Oswald, J., Henning, C., Sacramento, J., and Grewe, B. F · 2020
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Gradient surgery for multi-task learning
Yu, T., Kumar, S., Gupta, A., Levine, S., Hausman, K., and Finn, C · 2020
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Learning the pareto front with hypernetworks
Navon, A., Shamsian, A., Chechik, G., and Fetaya, E · 2021
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