Fetching the paper…
Reading the bibliography…
Multi-task learning (MTL) has been successfully used in many real-world applications, which aims to simultaneously solve multiple tasks with a single model.
Modeling task relationships in multi-task learning with multi-gate mixture-of-experts. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 1930–1939
Jiaqi Ma, Zhe Zhao, Xinyang Yi, Jilin Chen, Lichan Hong, and Ed H Chi. 2018b · 1939
Earlier work this paper cites.
Fitting segmented regression models by grid search
PM Lerman. 1980 · 1980
Earlier work this paper cites.
Adaptive mixtures of local experts
Robert A Jacobs, Michael I Jordan, Steven J Nowlan, and Geoffrey E Hinton. 1991 · 1991
Earlier work this paper cites.
A Bayesian/information theoretic model of learning to learn via multiple task sampling
Jonathan Baxter. 1997 · 1997
Earlier work this paper cites.
Multitask learning
Rich Caruana. 1997 · 1997
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
Earlier work this paper cites.
Cross-stitch networks for multi-task learning. In Proceedings of the IEEE conference on computer vision and pattern recognition . 3994–4003
Ishan Misra, Abhinav Shrivastava, Abhinav Gupta, and Martial Hebert. 2016 · 2016
Earlier work this paper cites.
DeepFM: a factorization-machine based neural network for CTR prediction
Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. 2017 · 2017
Earlier work this paper cites.
Neural episodic control. In International Conference on Machine Learning . PMLR, 2827–2836
Alexander Pritzel, Benigno Uria, Sriram Srinivasan, Adria Puigdomenech Badia, Oriol Vinyals, Demis Hassabis, Daan Wierstra, and Charles Blundell. 2017 · 2017
Earlier work this paper cites.
Set transformer: A framework for attention-based permutation-invariant neural networks. In International Conference on Machine Learning . PMLR, 3744–3753
Juho Lee, Yoonho Lee, Jungtaek Kim, Adam Kosiorek, Seungjin Choi, and Yee Whye Teh. 2019 · 2019
Earlier work this paper cites.
Latent multi-task architecture learning. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 33. 4822–4829
Sebastian Ruder, Joachim Bingel, Isabelle Augenstein, and Anders Søgaard. 2019 · 2019
Earlier work this paper cites.
Bert and pals: Projected attention layers for efficient adaptation in multi-task learning. In International Conference on Machine Learning . PMLR, 5986–5995
Asa Cooper Stickland and Iain Murray. 2019 · 2019
Cited alongside, same era.
Multiple relational attention network for multi-task learning. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 1123–1131
Jiejie Zhao, Bowen Du, Leilei Sun, Fuzhen Zhuang, Weifeng Lv, and Hui Xiong. 2019 · 2019
Cited alongside, same era.
Multi-task learning with deep neural networks: A survey
Michael Crawshaw. 2020 · 2020
Cited alongside, same era.
Progressive layered extraction (PLE): A novel multi-task learning (MTL) model for personalized recommendations. In Fourteenth ACM Conference on Recommender Systems . 269–278
Hongyan Tang, Junning Liu, Ming Zhao, and Xudong Gong. 2020 · 2020
Cited alongside, same era.
GemNN: gating-enhanced multi-task neural networks with feature interaction learning for CTR prediction. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval . 2166–2171
Hongliang Fei, Jingyuan Zhang, Xingxuan Zhou, Junhao Zhao, Xinyang Qi, and Ping Li. 2021 · 2021
Later among the works it cites.
Estimating true post-click conversion via group-stratified counterfactual inference
Tiankai Gu, Kun Kuang, Hong Zhu, Jingjie Li, Zhenhua Dong, Wenjie Hu, Zhenguo Li, Xiuqiang He, and Yue Liu. 2021 · 2021
Later among the works it cites.
Enhanced doubly robust learning for debiasing post-click conversion rate estimation. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval . 275–284
Siyuan Guo, Lixin Zou, Yiding Liu, Wenwen Ye, Suqi Cheng, Shuaiqiang Wang, Hechang Chen, Dawei Yin, and Yi Chang. 2021 · 2021
Later among the works it cites.
DSelect-k: Differentiable Selection in the Mixture of Experts with Applications to Multi-Task Learning
H. Hazimeh, Z. Zhao, A. Chowdhery, M. Sathiamoorthy, and E. H. Chi. 2021 · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Partoo Vafaeikia, Khashayar Namdar, and Farzad Khalvati. 2020 · 2020
Cited alongside, same era.
Revisiting multi-task learning in the deep learning era
Simon Vandenhende, Stamatios Georgoulis, Marc Proesmans, Dengxin Dai, and Luc Van Gool. 2020 · 2020
Cited alongside, same era.
K-adapter: Infusing knowledge into pre-trained models with adapters
Ruize Wang, Duyu Tang, Nan Duan, Zhongyu Wei, Xuanjing Huang, Guihong Cao, Daxin Jiang, Ming Zhou, et al · 2020
Cited alongside, same era.
Entire space multi-task modeling via post-click behavior decomposition for conversion rate prediction. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval . 2377–2386
Hong Wen, Jing Zhang, Yuan Wang, Fuyu Lv, Wentian Bao, Quan Lin, and Keping Yang. 2020 · 2020
Cited alongside, same era.
Large-scale causal approaches to debiasing post-click conversion rate estimation with multi-task learning. In Proceedings of The Web Conference 2020 . 2775–2781
Wenhao Zhang, Wentian Bao, Xiao-Yang Liu, Keping Yang, Quan Lin, Hong Wen, and Ramin Ramezani. 2020 · 2020
Cited alongside, same era.
Neural episodic control. In A deep multi-task representation learning method for time series classification and retrieval . Information Sciences, 17–32
Ling Chen, Donghui Chen, Fan Yang, and Jianling Sun. 2021 · 2021
Cited alongside, same era.
Entire space multi-task model: An effective approach for estimating post-click conversion rate. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval . 1137–1140
Xiao Ma, Liqin Zhao, Guan Huang, Zhi Wang, Zelin Hu, Xiaoqiang Zhu, and Kun Gai. 2018a
Cited in the paper.
An Analysis Of Entire Space Multi-Task Models For Post-Click Conversion Prediction. In Fifteenth ACM Conference on Recommender Systems . 613–619
Conor O’Brien, Kin Sum Liu, James Neufeld, Rafael Barreto, and Jonathan J Hunt. 2021 · 2021
Later among the works it cites.
Variational multi-task learning with gumbel-softmax priors
Jiayi Shen, Xiantong Zhen, Marcel Worring, and Ling Shao. 2021 · 2021
Later among the works it cites.
Hierarchically modeling micro and macro behaviors via multi-task learning for conversion rate prediction. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval . 2187–2191
Hong Wen, Jing Zhang, Fuyu Lv, Wentian Bao, Tianyi Wang, and Zulong Chen. 2021 · 2021
Later among the works it cites.
Modeling the sequential dependence among audience multi-step conversions with multi-task learning in targeted display advertising. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining . 3745–3755
Dongbo Xi, Zhen Chen, Peng Yan, Yinger Zhang, Yongchun Zhu, Fuzhen Zhuang, and Yu Chen. 2021 · 2021
Later among the works it cites.
Enhancing CTR Prediction with Context-Aware Feature Representation Learning
Fangye Wang, Yingxu Wang, Dongsheng Li, Hansu Gu, Tun Lu, Peng Zhang, and Ning Gu. 2022b · 2022
Later among the works it cites.
Hao Wang, Tai-Wei Chang, Tianqiao Liu, Jianmin Huang, Zhichao Chen, Chao Yu, Ruopeng Li, and Wei Chu. 2022a · 2022
Later among the works it cites.