[23] Casper Kaae Sønderby, Tapani Raiko, Lars Maaløe, Søren Kaae Sønderby, and Ole Winther. Ladder variational autoencoders. In Advances in Neural Information Processing Systems, 2016.
[24] Alex Kendall, Yarin Gal, and Roberto Cipolla. Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. In IEEE Conference on Computer Vision and Pattern Recognition, 2018.
[25] Shikun Liu, Edward Johns, and Andrew J Davison. End-to-end multi-task learning with attention. In IEEE Conference on Computer Vision and Pattern Recognition, 2019.
[26] Ishan Misra, Abhinav Shrivastava, Abhinav Gupta, and Martial Hebert. Cross-stitch networks for multi-task learning. In IEEE Conference on Computer Vision and Pattern Recognition, 2016.
[27] Liyang Liu, Yi Li, Zhanghui Kuang, Jing-Hao Xue, Yimin Chen, Wenming Yang, Qingmin Liao, and Wayne Zhang. Towards impartial multi-task learning. In International Conference on Learning Representations, 2020.
[28] Ozan Sener and Vladlen Koltun. Multi-task learning as multi-objective optimization. In Advances in Neural Information Processing Systems, 2018.
[29] Amir R Zamir, Alexander Sax, William Shen, Leonidas J Guibas, Jitendra Malik, and Silvio Savarese. Taskonomy: Disentangling task transfer learning. In Proceedings of the IEEE conference on computer vision and pattern recognition, 2018.
[30] Deblina Bhattacharjee, Tong Zhang, Sabine Susstrunk, and Mathieu Salzmann. Mult: an end-to-end multitask learning transformer. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022.
[31] Yi Zhang, Yu Zhang, and Wei Wang. Multi-task learning via generalized tensor trace norm. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2021.
[32] Jiayi Shen, Zehao Xiao, Xiantong Zhen, Cees GM Snoek, and Marcel Worring. Association graph learning for multi-task classification with category shifts. arXiv preprint arXiv:2210.04637, 2022.
[33] Yunlong Liang, Fandong Meng, Jinan Xu, Yufeng Chen, and Jie Zhou. Scheduled multi-task learning for neural chat translation. arXiv preprint arXiv:2205.03766, 2022.
[34] Yi Zhang, Yu Zhang, and Wei Wang. Learning linear and nonlinear low-rank structure in multi-task learning. IEEE Transactions on Knowledge and Data Engineering, 2022.
[35] Bart Bakker and Tom Heskes. Task clustering and gating for bayesian multitask learning. Journal of Machine Learning Research, 2003.
[36] Kai Yu, Volker Tresp, and Anton Schwaighofer. Learning gaussian processes from multiple tasks. In International Conference on Machine Learning, 2005.
[37] Michalis K Titsias and Miguel Lázaro-Gredilla. Spike and slab variational inference for multi-task and multiple kernel learning. In Advances in neural information processing systems, 2011.
[38] Neil D Lawrence and John C Platt. Learning to learn with the informative vector machine. In International Conference on Machine Learning, 2004.
[39] Fariba Yousefi, Michael Thomas Smith, and Mauricio A Álvarez. Multi-task learning for aggregated data using gaussian processes. arXiv preprint arXiv:1906.09412, 2019.
[40] Diane Oyen and Terran Lane. Leveraging domain knowledge in multitask bayesian network structure learning. In Proceedings of the AAAI Conference on Artificial Intelligence, 2012.
[41] Weizhu Qian, Bowei Chen, Yichao Zhang, Guanghui Wen, and Franck Gechter. Multi-task variational information bottleneck. arXiv preprint arXiv:2007.00339, 2020.
[42] Gjorgji Strezoski, Nanne van Noord, and Marcel Worring. Learning task relatedness in multi-task learning for images in context. In International Conference on Multimedia Retrieval, 2019.
[43] Ximeng Sun, Rameswar Panda, Rogerio Feris, and Kate Saenko. Adashare: Learning what to share for efficient deep multi-task learning. arXiv preprint arXiv:1911.12423, 2019.
[44] Gjorgji Strezoski, Nanne van Noord, and Marcel Worring. Many task learning with task routing. In IEEE International Conference on Computer Vision, 2019.