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Auxiliary-Task Learning (ATL) aims to improve the performance of the target task by leveraging the knowledge obtained from related tasks.
To transfer or not to transfer
Michael T. Rosenstein · 2005
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Domain adaptation with multiple sources
Yishay Mansour, Mehryar Mohri, and Afshin Rostamizadeh · 2008
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Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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A survey on transfer learning
Sinno Jialin Pan and Qiang Yang · 2010
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
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New analysis and algorithm for learning with drifting distributions
Mehryar Mohri and Andres Muñoz Medina · 2012
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Indoor segmentation and support inference from rgbd images
Nathan Silberman, Derek Hoiem, Pushmeet Kohli, and Rob Fergus · 2012
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Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Dong-Hyun Lee · 2013
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Discriminative unsupervised feature learning with convolutional neural networks
Alexey Dosovitskiy, Jost Tobias Springenberg, Martin Riedmiller, and Thomas Brox · 2014
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Qualitatively characterizing neural network optimization problems
Ian Goodfellow, Oriol Vinyals, and Andrew Saxe · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Cross-stitch networks for multi-task learning
Ishan Misra, Abhinav Shrivastava, Abhinav Gupta, and Martial Hebert · 2016
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Andrei A. Rusu, Neil C. Rabinowitz, Guillaume Desjardins, Hubert Soyer, James Kirkpatrick, Koray Kavukcuoglu, Razvan Pascanu, and Raia Hadsell · 2016
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Wide residual networks
Sergey Zagoruyko and Nikos Komodakis · 2016
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Deep reinforcement learning from human preferences
Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei · 2017
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Pathnet: Evolution channels gradient descent in super neural networks
Chrisantha Fernando, Dylan Banarse, Charles Blundell, Yori Zwols, David Ha, Andrei A. Rusu, Alexander Pritzel, and Daan Wierstra · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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Accurate, large minibatch SGD: training imagenet in 1 hour
Priya Goyal, Piotr Dollár, Ross B. Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, and Kaiming He · 2017
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Mask r-cnn
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2017
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Ubernet: Training a ‘universal’ convolutional neural network for low-, mid-, and high-level vision using diverse datasets and limited memory
Iasonas Kokkinos · 2017
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Temporal ensembling for semi-supervised learning
Samuli Laine and Timo Aila · 2017
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SGDR: stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2017
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Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Antti Tarvainen and Harri Valpola · 2017
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Encoder-decoder with atrous separable convolution for semantic image segmentation
Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam · 2018
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Gradnorm: Gradient normalization for adaptive loss balancing in deep multitask networks
Zhao Chen, Vijay Badrinarayanan, Chen-Yu Lee, and Andrew Rabinovich · 2018
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Adapting auxiliary losses using gradient similarity
Yunshu Du, Wojciech M Czarnecki, Siddhant M Jayakumar, Mehrdad Farajtabar, Razvan Pascanu, and Balaji Lakshminarayanan · 2018
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Unsupervised representation learning by predicting image rotations
Spyros Gidaris, Praveer Singh, and Nikos Komodakis · 2018
Cited alongside, same era.
Multi-task learning using uncertainty to weigh losses for scene geometry and semantics
Alex Kendall, Yarin Gal, and Roberto Cipolla · 2018
Cited alongside, same era.
Visualizing the loss landscape of neural nets
Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, and Tom Goldstein · 2018
Cited alongside, same era.
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
Cited alongside, same era.
Entire space multi-task model: An effective approach for estimating post-click conversion rate
Xiao Ma, Liqin Zhao, Guan Huang, Zhi Wang, Zelin Hu, Xiaoqiang Zhu, and Kun Gai · 2018
Cited alongside, same era.
Progressive layered extraction (ple): A novel multi-task learning (mtl) model for personalized recommendations
Hongyan Tang, Junning Liu, Ming Zhao, and Xudong Gong · 2020
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Reducing BERT pre-training time from 3 days to 76 minutes
Yang You, Jing Li, Jonathan Hseu, Xiaodan Song, James Demmel, and Cho-Jui Hsieh · 2020
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Gradient surgery for multi-task learning
Tianhe Yu, Saurabh Kumar, Abhishek Gupta, Sergey Levine, Karol Hausman, and Chelsea Finn · 2020
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Auxiliary task update decomposition: The good, the bad and the neutral
Lucio M Dery, Yann Dauphin, and David Grangier · 2021
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Efficiently identifying task groupings for multi-task learning
Christopher Fifty, Ehsan Amid, Zhe Zhao, Tianhe Yu, Rohan Anil, and Chelsea Finn · 2021
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Multitask-centernet (mcn): Efficient and diverse multitask learning using an anchor free approach
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Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, and Shin Ishii · 2018
Cited alongside, same era.
Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever · 2018
Cited alongside, same era.
Multi-task learning as multi-objective optimization
Ozan Sener and Vladlen Koltun · 2018
Cited alongside, same era.
Taskonomy: Disentangling task transfer learning
Amir Roshan Zamir, Alexander Sax, William B. Shen, Leonidas J. Guibas, Jitendra Malik, and Silvio Savarese · 2018
Cited alongside, same era.
Importance weighted adversarial nets for partial domain adaptation
Jing Zhang, Zewei Ding, Wanqing Li, and Philip Ogunbona · 2018
Cited alongside, same era.
Catastrophic forgetting meets negative transfer: Batch spectral shrinkage for safe transfer learning
Xinyang Chen, Sinan Wang, Bo Fu, Mingsheng Long, and Jianmin Wang · 2019
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
Cited alongside, same era.
Falk Heuer, Sven Mantowsky, Saqib Bukhari, and Georg Schneider · 2021
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Conflict-averse gradient descent for multi-task learning
Bo Liu, Xingchao Liu, Xiaojie Jin, Peter Stone, and Qiang Liu · 2021
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Towards impartial multi-task learning
L Liu, Y Li, Z Kuang, J Xue, Y Chen, W Yang, Q Liao, and Wayne Zhang · 2021
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Revisiting the calibration of modern neural networks
Matthias Minderer, Josip Djolonga, Rob Romijnders, Frances Hubis, Xiaohua Zhai, Neil Houlsby, Dustin Tran, and Mario Lucic · 2021
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Auxiliary learning by implicit differentiation
Aviv Navon, Idan Achituve, Haggai Maron, Gal Chechik, and Ethan Fetaya · 2021
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Gradient vaccine: Investigating and improving multi-task optimization in massively multilingual models
Zirui Wang, Yulia Tsvetkov, Orhan Firat, and Yuan Cao · 2021
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Learning to expand audience via meta hybrid experts and critics for recommendation and advertising
Yongchun Zhu, Yudan Liu, Ruobing Xie, Fuzhen Zhuang, Xiaobo Hao, Kaikai Ge, Xu Zhang, Leyu Lin, and Juan Cao · 2021
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Weighted training for cross-task learning
Shuxiao Chen, Koby Crammer, Hangfeng He, Dan Roth, and Weijie J Su · 2022
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Cold fusion: Collaborative descent for distributed multitask finetuning
Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, and Leshem Choshen · 2022
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Rotograd: Gradient homogenization in multitask learning
Adrián Javaloy and Isabel Valera · 2022
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Transferability in deep learning: A survey
Junguang Jiang, Yang Shu, Jianmin Wang, and Mingsheng Long · 2022
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In defense of the unitary scalarization for deep multi-task learning
Vitaly Kurin, Alessandro De Palma, Ilya Kostrikov, Shimon Whiteson, and M Pawan Kumar · 2022
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Reasonable effectiveness of random weighting: A litmus test for multi-task learning
Baijiong Lin, YE Feiyang, Yu Zhang, and Ivor Tsang · 2022
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LibMTL: A python library for multi-task learning
Baijiong Lin and Yu Zhang · 2022
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Auto-lambda: Disentangling dynamic task relationships
Shikun Liu, Stephen James, Andrew J Davison, and Edward Johns · 2022
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Multi-task learning as a bargaining game
Aviv Navon, Aviv Shamsian, Idan Achituve, Haggai Maron, Kenji Kawaguchi, Gal Chechik, and Ethan Fetaya · 2022
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Introducing chatgpt, 2022
OpenAI · 2022
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Efficient and effective multi-task grouping via meta learning on task combinations
Xiaozhuang Song, Shun Zheng, Wei Cao, James Yu, and Jiang Bian · 2022
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Do current multi-task optimization methods in deep learning even help?
Derrick Xin, Behrooz Ghorbani, Justin Gilmer, Ankush Garg, and Orhan Firat · 2022
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A survey on negative transfer
Wen Zhang, Lingfei Deng, Lei Zhang, and Dongrui Wu · 2022
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Aang: Automating auxiliary learning
Lucio M Dery, Paul Michel, Mikhail Khodak, Graham Neubig, and Ameet Talwalkar · 2023
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Gpt-4 technical report, 2023
OpenAI · 2023
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Auxiliary learning as an asymmetric bargaining game
Aviv Shamsian, Aviv Navon, Neta Glazer, Kenji Kawaguchi, Gal Chechik, and Ethan Fetaya · 2023
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