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In Multi-task learning (MTL), a joint model is trained to simultaneously make predictions for several tasks.
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Zhang, Z., Shi, J., Chen, H.-H., Guizani, M., and Qiu, P · 2008
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Smart carrier sensing for distributed computation of the generalized nash bargaining solution
Leshem, A. and Zehavi, E · 2011
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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
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Revisiting Frank-Wolfe: Projection-free sparse convex optimization
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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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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
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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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Variations and extension of the convex–concave procedure
Lipp, T. and Boyd, S · 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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Segnet: A deep convolutional encoder-decoder architecture for image segmentation
Badrinarayanan, V., Kendall, A., and Cipolla, R · 2017
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Neural message passing for quantum chemistry
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., and Dahl, G. E · 2017
Provably powerful graph networks
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First-order methods almost always avoid saddle points: The case of vanishing step-sizes
Panageas, I., Piliouras, G., and Wang, X · 2019
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Ray interference: a source of plateaus in deep reinforcement learning
Schaul, T., Borsa, D., Modayil, J., and Pascanu, R · 2019
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Regularizing deep multi-task networks using orthogonal gradients
Suteu, M. and Guo, Y · 2019
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The Kalai-Smorodinsky solution for many-objective Bayesian optimization
Binois, M., Picheny, V., Taillandier, P., and Habbal, A · 2020
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A joint many-task model: Growing a neural network for multiple nlp tasks
Hashimoto, K., Xiong, C., Tsuruoka, Y., and Socher, R · 2017
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Learning to push by grasping: Using multiple tasks for effective learning
Pinto, L. and Gupta, A · 2017
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An overview of multi-task learning in deep neural networks
Ruder, S · 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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Adapting auxiliary losses using gradient similarity
Du, Y., Czarnecki, W. M., Jayakumar, S. M., Farajtabar, M., Pascanu, R., and Lakshminarayanan, B · 2018
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Soft actor-critic: Off-policy maximum entropy deep reinforcement, 2018
Haarnoja, T., Zhou, A., Abbeel, P., and Levine, S · 2018
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Just pick a sign: Optimizing deep multitask models with gradient sign dropout
Chen, Z., Ngiam, J., Huang, Y., Luong, T., Kretzschmar, H., Chai, Y., and Anguelov, D · 2020
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Multi-task learning with deep neural networks: A survey
Crawshaw, M · 2020
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Directional message passing for molecular graphs
Klicpera, J., Groß, J., and Günnemann, S · 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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Gradient vaccine: Investigating and improving multi-task optimization in massively multilingual models
Wang, Z., Tsvetkov, Y., Firat, O., and Cao, Y · 2020
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Multi-task reinforcement learning with soft modularization
Yang, R., Xu, H., Wu, Y., and Wang, X · 2020
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Self-supervised learning for domain adaptation on point clouds
Achituve, I., Maron, H., and Chechik, G · 2021
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Fair exploration via axiomatic bargaining
Baek, J. and Farias, V. F · 2021
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Rotograd: Dynamic gradient homogenization for multi-task learning
Javaloy, A. and Valera, I · 2021
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Cooperative federated learning-based task offloading scheme for tactical edge networks
Kim, S · 2021
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A closer look at loss weighting in multi-task learning
Lin, B., Ye, F., and Zhang, Y · 2021
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GBK-means clustering algorithm: An improvement to the K-means algorithm based on the bargaining game
Rezaee, M. J., Eshkevari, M., Saberi, M., and Hussain, O · 2021
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Multi-task reinforcement learning with context-based representations
Sodhani, S., Zhang, A., and Pineau, J · 2021
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