Fetching the paper…
Reading the bibliography…
Multi-task learning (MTL) aims to empower a model to tackle multiple tasks simultaneously.
On the mathematical foundations of theoretical statistics
Ronald A Fisher · 1922
Earlier work this paper cites.
A mathematical theory of communication
Claude Elwood Shannon · 1948
Earlier work this paper cites.
Multitask learning
Rich Caruana · 1997
Earlier work this paper cites.
The mnist database of handwritten digits
Yann LeCun · 1998
Earlier work this paper cites.
Semi-supervised learning by entropy minimization
Yves Grandvalet and Yoshua Bengio · 2004
Earlier work this paper cites.
Spearman correlation coefficients, differences between
Leann Myers and Maria J Sirois · 2004
Earlier work this paper cites.
A unified architecture for natural language processing: Deep neural networks with multitask learning
Ronan Collobert and Jason Weston · 2008
Earlier work this paper cites.
The german traffic sign recognition benchmark: a multi-class classification competition
Johannes Stallkamp, Marc Schlipsing, Jan Salmen, and Christian Igel · 2011
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Netzer Yuval · 2011
Earlier work this paper cites.
3d object representations for fine-grained categorization
Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei · 2013
Earlier work this paper cites.
Describing textures in the wild
Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, and Andrea Vedaldi · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson · 2014
Earlier work this paper cites.
Multi-task learning for multiple language translation
Daxiang Dong, Hua Wu, Wei He, Dianhai Yu, and Haifeng Wang · 2015
Earlier work this paper cites.
Cross-stitch networks for multi-task learning
Ishan Misra, Abhinav Shrivastava, Abhinav Gupta, and Martial Hebert · 2016
Earlier work this paper cites.
Sun database: Exploring a large collection of scene categories
Jianxiong Xiao, Krista A Ehinger, James Hays, Antonio Torralba, and Aude Oliva · 2016
Earlier work this paper cites.
Remote sensing image scene classification: Benchmark and state of the art
Gong Cheng, Junwei Han, and Xiaoqiang Lu · 2017
Earlier work this paper cites.
Fully-adaptive feature sharing in multi-task networks with applications in person attribute classification
Yongxi Lu, Abhishek Kumar, Shuangfei Zhai, Yu Cheng, Tara Javidi, and Rogerio Feris · 2017
Earlier work this paper cites.
Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
Earlier work this paper cites.
Gradnorm: Gradient normalization for adaptive loss balancing in deep multitask networks
Zhao Chen, Vijay Badrinarayanan, Chen-Yu Lee, and Andrew Rabinovich · 2018
Earlier work this paper cites.
Modeling task relationships in multi-task learning with multi-gate mixture-of-experts
Jiaqi Ma, Zhe Zhao, Xinyang Yi, Jilin Chen, Lichan Hong, and Ed H. Chi · 2018
Earlier work this paper cites.
Multi-task learning as multi-objective optimization
Ozan Sener and Vladlen Koltun · 2018
Earlier work this paper cites.
BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
Earlier work this paper cites.
Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification
Patrick Helber, Benjamin Bischke, Andreas Dengel, and Damian Borth · 2019
Cited alongside, same era.
Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2019
Cited alongside, same era.
On the convergence of fedavg on non-iid data
Xiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, and Zhihua Zhang · 2019
Cited alongside, same era.
Grid search, random search, genetic algorithm: a big comparison for nas
Petro Liashchynskyi and Pavlo Liashchynskyi · 2019
Cited alongside, same era.
Just pick a sign: Optimizing deep multitask models with gradient sign dropout
Zhao Chen, Jiquan Ngiam, Yanping Huang, Thang Luong, Henrik Kretzschmar, Yuning Chai, and Dragomir Anguelov · 2020
Cited alongside, same era.
Metabalance: Improving multi-task recommendations via adapting gradient magnitudes of auxiliary tasks
Yun He, Xue Feng, Cheng Cheng, Geng Ji, Yunsong Guo, and James Caverlee · 2022
Later among the works it cites.
Deep neural network fusion via graph matching with applications to model ensemble and federated learning
Chang Liu, Chenfei Lou, Runzhong Wang, Alan Yuhan Xi, Li Shen, and Junchi Yan · 2022
Later among the works it cites.
Merging models with fisher-weighted averaging
Michael S Matena and Colin A Raffel · 2022
Later among the works it cites.
Efficient test-time model adaptation without forgetting
Shuaicheng Niu, Jiaxiang Wu, Yifan Zhang, Yaofo Chen, Shijian Zheng, Peilin Zhao, and Mingkui Tan · 2022
Later among the works it cites.
Uncertainty-guided source-free domain adaptation
Subhankar Roy, Martin Trapp, Andrea Pilzer, Juho Kannala, Nicu Sebe, Elisa Ricci, and Arno Solin · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Learning to branch for multi-task learning
Pengsheng Guo, Chen-Yu Lee, and Daniel Ulbricht · 2020
Cited alongside, same era.
Stochastic weight averaging in parallel: Large-batch training that generalizes well
Vipul Gupta, Santiago Akle Serrano, and Dennis DeCoste · 2020
Cited alongside, same era.
Versatile black-box optimization
Jialin Liu, Antoine Moreau, Mike Preuss, Jeremy Rapin, Baptiste Roziere, Fabien Teytaud, and Olivier Teytaud · 2020
Cited alongside, same era.
Pre-trained models for natural language processing: A survey
Xipeng Qiu, Tianxiang Sun, Yige Xu, Yunfan Shao, Ning Dai, and Xuanjing Huang · 2020
Cited alongside, same era.
Which tasks should be learned together in multi-task learning?
Trevor Standley, Amir Zamir, Dawn Chen, Leonidas Guibas, Jitendra Malik, and Silvio Savarese · 2020
Cited alongside, same era.
Adashare: Learning what to share for efficient deep multi-task learning
Ximeng Sun, Rameswar Panda, Rogerio Feris, and Kate Saenko · 2020
Cited alongside, same era.
Federated learning with matched averaging
Hongyi Wang, Mikhail Yurochkin, Yuekai Sun, Dimitris Papailiopoulos, and Yasaman Khazaeni · 2020
Cited alongside, same era.
Zhenyi Wang, Xiaoyang Wang, Li Shen, Qiuling Suo, Kaiqiang Song, Dong Yu, Yan Shen, and Mingchen Gao · 2022
Later among the works it cites.
Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
Mitchell Wortsman, Gabriel Ilharco, Samir Ya Gadre, Rebecca Roelofs, Raphael Gontijo-Lopes, Ari S Morcos, Hongseok Namkoong, Ali Farhadi, Yair Carmon, Simon Kornblith, et al · 2022
Later among the works it cites.
Git re-basin: Merging models modulo permutation symmetries
Samuel Ainsworth, Jonathan Hayase, and Siddhartha Srinivasa · 2023
Closest in time.
Lorahub: Efficient cross-task generalization via dynamic lora composition
Chengsong Huang, Qian Liu, Bill Yuchen Lin, Tianyu Pang, Chao Du, and Min Lin · 2023
Closest in time.
Editing models with task arithmetic
Gabriel Ilharco, Marco Tulio Ribeiro, Mitchell Wortsman, Ludwig Schmidt, Hannaneh Hajishirzi, and Ali Farhadi · 2023
Closest in time.
Dataless knowledge fusion by merging weights of language models
Xisen Jin, Xiang Ren, Daniel Preotiuc-Pietro, and Pengxiang Cheng · 2023
Closest in time.
Weishi Li, Yong Peng, Miao Zhang, Liang Ding, Han Hu, and Li Shen · 2023
Closest in time.
A comprehensive survey on test-time adaptation under distribution shifts
Jian Liang, Ran He, and Tieniu Tan · 2023
Closest in time.
Towards stable test-time adaptation in dynamic wild world
Shuaicheng Niu, Jiaxiang Wu, Yifan Zhang, Zhiquan Wen, Yaofo Chen, Peilin Zhao, and Mingkui Tan · 2023
Closest in time.
Task arithmetic in the tangent space: Improved editing of pre-trained models
Guillermo Ortiz-Jimenez, Alessandro Favero, and Pascal Frossard · 2023
Closest in time.
Zipit! merging models from different tasks without training
George Stoica, Daniel Bolya, Jakob Bjorner, Taylor Hearn, and Judy Hoffman · 2023
Closest in time.
Distributionally robust memory evolution with generalized divergence for continual learning
Zhenyi Wang, Li Shen, Tiehang Duan, Qiuling Suo, Le Fang, Wei Liu, and Mingchen Gao · 2023
Closest in time.
pi-tuning: Transferring multimodal foundation models with optimal multi-task interpolation
Chengyue Wu, Teng Wang, Yixiao Ge, Zeyu Lu, Ruisong Zhou, Ying Shan, and Ping Luo · 2023
Closest in time.
Resolving interference when merging models
Prateek Yadav, Derek Tam, Leshem Choshen, Colin Raffel, and Mohit Bansal · 2023
Closest in time.
Adatask: A task-aware adaptive learning rate approach to multi-task learning
Enneng Yang, Junwei Pan, Ximei Wang, Haibin Yu, Li Shen, Xihua Chen, Lei Xiao, Jie Jiang, and Guibing Guo · 2023
Closest in time.
Composing parameter-efficient modules with arithmetic operations
Jinghan Zhang, Shiqi Chen, Junteng Liu, and Junxian He · 2023
Closest in time.
Forkmerge: Mitigating negative transfer in auxiliary-task learning
Junguang Jiang, Baixu Chen, Junwei Pan, Ximei Wang, Dapeng Liu, Jie Jiang, and Mingsheng Long · 2024
Closest in time.
Multi-scenario and multi-task aware feature interaction for recommendation system
Derun Song, Enneng Yang, Guibing Guo, Li Shen, Linying Jiang, and Xingwei Wang · 2024
Closest in time.