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By jointly learning multiple tasks, multi-task learning (MTL) can leverage the shared knowledge across tasks, resulting in improved data efficiency and generalization performance.
A quantitative measure of fairness and discrimination
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Achieving mac layer fairness in wireless packet networks
Nandagopal, T., Kim, T.-E., Gao, X., and Bharghavan, V · 2000
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Regularized multi–task learning
Evgeniou, T. and Pontil, M · 2004
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Joint congestion control, routing, and mac for stability and fairness in wireless networks
Eryilmaz, A. and Srikant, R · 2006
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A unified framework for max-min and min-max fairness with applications
Radunovic, B. and Le Boudec, J.-Y · 2007
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Taking advantage of sparsity in multi-task learning
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An axiomatic theory of fairness in network resource allocation
Lan, T., Kao, D., Chiang, M., and Sabharwal, A · 2010
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Wang, Z., Tsvetkov, Y., Firat, O., and Cao, Y · 2010
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Multiple-gradient descent algorithm (mgda) for multiobjective optimization
Désidéri, J.-A · 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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Fairness in wireless networks: Issues, measures and challenges
Huaizhou, S., Prasad, R. V., Onur, E., and Niemegeers, I · 2013
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Communication networks: an optimization, control, and stochastic networks perspective
Srikant, R. and Ying, L · 2013
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Optimal resource allocation in full-duplex wireless-powered communication network
Ju, H. and Zhang, R · 2014
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Quantum chemistry structures and properties of 134 kilo molecules
Ramakrishnan, R., Dral, P. O., Rupp, M., and von Lilienfeld, O. A · 2014
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Facial landmark detection by deep multi-task learning
Zhang, Z., Luo, P., Loy, C. C., and Tang, X · 2014
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Optimal resource allocation in complex communication networks
Liu, H. and Xia, Y · 2015
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Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X · 2015
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Ask the gru: Multi-task learning for deep text recommendations
Bansal, T., Belanger, D., and McCallum, A · 2016
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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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Instance-aware semantic segmentation via multi-task network cascades
Dai, J., He, K., and Sun, J · 2016
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Segnet: A deep convolutional encoder-decoder architecture for image segmentation
Badrinarayanan, V., Kendall, A., and Cipolla, R · 2017
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Consistent multitask learning with nonlinear output relations
Ciliberto, C., Rudi, A., Rosasco, L., and Pontil, M · 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
Towards impartial multi-task learning
Liu, L., Li, Y., Kuang, Z., Xue, J.-H., Chen, Y., Yang, W., Liao, Q., and Zhang, W · 2020
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A survey on resource allocation in vehicular networks
Noor-A-Rahim, M., Liu, Z., Lee, H., Ali, G. M. N., Pesch, D., and Xiao, P · 2020
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Efficient multitask feature and relationship learning
Zhao, H., Stretcu, O., Smola, A. J., and Gordon, G. J · 2020
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Multi-task learning with attention for end-to-end autonomous driving
Ishihara, K., Kanervisto, A., Miura, J., and Hautamaki, V · 2021
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Reasonable effectiveness of random weighting: A litmus test for multi-task learning
Lin, B., Ye, F., Zhang, Y., and Tsang, I. W · 2021
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Conflict-averse gradient descent for multi-task learning
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Kokkinos, I · 2017
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An overview of multi-task learning in deep neural networks
Ruder, S · 2017
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Haarnoja, T., Zhou, A., Abbeel, P., and Levine, S · 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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Multi-task learning as multi-objective optimization
Sener, O. and Koltun, V · 2018
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A brief review on multi-task learning
Thung, K.-H. and Wee, C.-Y · 2018
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Fast graph representation learning with pytorch geometric
Fey, M. and Lenssen, J. E · 2019
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Liu, B., Liu, X., Jin, X., Stone, P., and Liu, Q · 2021
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Mtrl - multi task rl algorithms
Shagun Sodhani, A. Z · 2021
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Multi-task learning for dense prediction tasks: A survey
Vandenhende, S., Georgoulis, S., Van Gansbeke, W., Proesmans, M., Dai, D., and Van Gool, L · 2021
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A survey on resource allocation for 5g heterogeneous networks: Current research, future trends, and challenges
Xu, Y., Gui, G., Gacanin, H., and Adachi, F · 2021
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A survey on multi-task learning
Zhang, Y. and Yang, Q · 2021
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Mitigating gradient bias in multi-objective learning: A provably convergent approach
Fernando, H. D., Shen, H., Liu, M., Chaudhury, S., Murugesan, K., and Chen, T · 2022
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Multi-task learning as a bargaining game
Navon, A., Shamsian, A., Achituve, I., Maron, H., Kawaguchi, K., Chechik, G., and Fetaya, E · 2022
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Proportional fairness in federated learning
Zhang, G., Malekmohammadi, S., Chen, X., and Yu, Y · 2022
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Three-way trade-off in multi-objective learning: Optimization, generalization and conflict-avoidance
Chen, L., Fernando, H., Ying, Y., and Chen, T · 2023
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Revisiting scalarization in multi-task learning: A theoretical perspective
Hu, Y., Xian, R., Wu, Q., Fan, Q., Yin, L., and Zhao, H · 2023
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Famo: Fast adaptive multitask optimization
Liu, B., Feng, Y., Stone, P., and Liu, Q · 2023
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Direction-oriented multi-objective learning: Simple and provable stochastic algorithms
Xiao, P., Ban, H., and Ji, K · 2023
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A survey of multi-task learning in natural language processing: Regarding task relatedness and training methods
Zhang, Z., Yu, W., Yu, M., Guo, Z., and Jiang, M · 2023
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On the convergence of stochastic multi-objective gradient manipulation and beyond
Zhou, S., Zhang, W., Jiang, J., Zhong, W., Gu, J., and Zhu, W · 2023
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