A list of papers that are relevant to multi-task machine learning. Inspired by awesome-deep-learning-papers and awesome-adversarial-machine-learning.
- PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning.
Arun Mallya, Svetlana Lazebnik.
CVPR 2018. - Pseudo-task Augmentation: From Deep Multitask Learning to Intratask Sharing—and Back.
Elliot Meyerson, Risto Miikkulainen.
ICML 2018. - Beyond Shared Hierarchies: Deep Multitask Learning through Soft Layer Ordering.
Elliot Meyerson, Risto Miikkulainen.
ICLR 2018. - Routing Networks: Adaptive Selection of Non-linear Functions for Multi-Task Learning.
Clemens Rosenbaum, Tim Klinger, Matthew Riemer.
ICLR 2018. - Unifying and Merging Well-trained Deep Neural Networks for Inference Stage.
Yi-Min Chou, Yi-Ming Chan, Jia-Hong Lee, Chih-Yi Chiu, Chu-Song Chen.
IJCAI-ECAI 2018. - Multi-task Sequence to Sequence Learning.
Minh-Thang Luong, Quoc V. Le, Ilya Sutskever, Oriol Vinyals, Lukasz Kaiser.
ICLR 2016.
- Cross-stitch Networks for Multi-task Learning.
Ishan Misra, Abhinav Shrivastava, Abhinav Gupta, Martial Hebert.
CVPR 2016.
- Evolutionary Architecture Search For Deep Multitask Networks.
Jason Liang, Elliot Meyerson, Risto Miikkulainen.
Genetic and Evolutionary Computation Conference (GECCO) 2018. - Transfer Learning to Learn with Multitask Neural Model Search.
Catherine Wong, Andrea Gesmundo.
arXiv preprint 2017.
- AutoLoss: Learning Discrete Schedules for Alternate Optimization.
Haowen Xu, Hao Zhang, Zhiting Hu, Xiaodan Liang, Ruslan Salakhutdinov, Eric Xing.
arXiv preprint 2018. - Dynamic Task Prioritization for Multitask Learning.
Michelle Guo, Albert Haque, De-An Huang, Serena Yeung, Li Fei-Fei.
ECCV 2018. - Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics.
Alex Kendall, Yarin Gal, Roberto Cipolla.
CVPR 2018. - An Overview of Multi-Task Learning in Deep Neural Networks.
Sebastian Ruder.
arXiv preprint 2017. - A Survey on Multi-Task Learning.
Yu Zhang, Qiang Yang.
arXiv preprint 2017.