Fetching the paper…
Reading the bibliography…
We propose generative multitask learning (GMTL), a simple and scalable approach to causal representation learning for multitask learning.
Multitask learning
Rich Caruana · 1997
Earlier work this paper cites.
A weighted kendall’s tau statistic
Grace S Shieh · 1998
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Fei-Fei Li · 2009
Earlier work this paper cites.
Causality
Judea Pearl · 2009
Earlier work this paper cites.
Describing people: A poselet-based approach to attribute classification
Lubomir D. Bourdev, Subhransu Maji, and Jitendra Malik · 2011
Earlier work this paper cites.
On causal and anticausal learning
Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, and Joris M. Mooij · 2012
Earlier work this paper cites.
Domain adaptation under target and conditional shift
Kun Zhang, Bernhard Schölkopf, Krikamol Muandet, and Zhikun Wang · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
Earlier work this paper cites.
Deep neural networks for youtube recommendations
Paul Covington, Jay Adams, and Emre Sargin · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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.
"why should I trust you?": Explaining the predictions of any classifier
Marco Túlio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
Earlier work this paper cites.
When is multitask learning effective? semantic sequence prediction under varying data conditions
Héctor Martínez Alonso and Barbara Plank · 2017
Earlier work this paper cites.
Measuring the tendency of cnns to learn surface statistical regularities
Jason Jo and Yoshua Bengio · 2017
Earlier work this paper cites.
An overview of multi-task learning in deep neural networks
Sebastian Ruder · 2017
Earlier work this paper cites.
A survey on multi-task learning
Yu Zhang and Qiang Yang · 2017
Earlier work this paper cites.
Auxiliary tasks in multi-task learning
Lukas Liebel and Marco Körner · 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.
The natural language decathlon: Multitask learning as question answering
Bryan McCann, Nitish Shirish Keskar, Caiming Xiong, and Richard Socher · 2018
Cited alongside, same era.
Deep neural networks are more accurate than humans at detecting sexual orientation from facial images
Yilun Wang and Michal Kosinski · 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.
Invariant risk minimization
Martín Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz · 2019
Shortcut learning in deep neural networks
Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard S. Zemel, Wieland Brendel, Matthias Bethge, and Felix A. Wichmann · 2020
Later among the works it cites.
Which tasks should be learned together in multi-task learning?
Trevor Standley, Amir Roshan Zamir, Dawn Chen, Leonidas J. Guibas, Jitendra Malik, and Silvio Savarese · 2020
Later among the works it cites.
A brief review of deep multi-task learning and auxiliary task learning
Partoo Vafaeikia, Khashayar Namdar, and Farzad Khalvati · 2020
Later among the works it cites.
Multi-task learning for natural language processing in the 2020s: where are we going?
Joseph Worsham and Jugal Kalita · 2020
Later among the works it cites.
Robust learning through cross-task consistency
Amir Roshan Zamir, Alexander Sax, Nikhil Cheerla, Rohan Suri, Zhangjie Cao, Jitendra Malik, and Leonidas J. Guibas · 2020
Later among the works it cites.
Muppet: Massive multi-task representations with pre-finetuning
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Multinet: Multi-modal multi-task learning for autonomous driving
Sauhaarda Chowdhuri, Tushar Pankaj, and Karl Zipser · 2019
Cited alongside, same era.
Multi-task deep neural networks for natural language understanding
Xiaodong Liu, Pengcheng He, Weizhu Chen, and Jianfeng Gao · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
Cited alongside, same era.
Do imagenet classifiers generalize to imagenet?
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar · 2019
Cited alongside, same era.
Causality for machine learning
Bernhard Schölkopf · 2019
Cited alongside, same era.
Preventing failures due to dataset shift: Learning predictive models that transport
Adarsh Subbaswamy, Peter Schulam, and Suchi Saria · 2019
Cited alongside, same era.
Armen Aghajanyan, Anchit Gupta, Akshat Shrivastava, Xilun Chen, Luke Zettlemoyer, and Sonal Gupta · 2021
Later among the works it cites.
Deep learning for AI
Yoshua Bengio, Yann LeCun, and Geoffrey E. Hinton · 2021
Later among the works it cites.
In search of lost domain generalization
Ishaan Gulrajani and David Lopez-Paz · 2021
Later among the works it cites.
Mt-opt: Continuous multi-task robotic reinforcement learning at scale
Dmitry Kalashnikov, Jacob Varley, Yevgen Chebotar, Benjamin Swanson, Rico Jonschkowski, Chelsea Finn, Sergey Levine, and Karol Hausman · 2021
Later among the works it cites.
Triage of 2d mammographic images using multi-view multi-task convolutional neural networks
Trent Kyono, Fiona J Gilbert, and Mihaela Van Der Schaar · 2021
Later among the works it cites.
Nonlinear invariant risk minimization: A causal approach
Chaochao Lu, Yuhuai Wu, José Miguel Hernández-Lobato, and Bernhard Schölkopf · 2021
Later among the works it cites.
A survey on bias and fairness in machine learning
Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan · 2021
Later among the works it cites.
Predictive modeling in the presence of nuisance-induced spurious correlations
Aahlad Manas Puli, Lily H. Zhang, Eric K. Oermann, and Rajesh Ranganath · 2021
Later among the works it cites.
Towards causal representation learning
Bernhard Schölkopf, Francesco Locatello, Stefan Bauer, Nan Rosemary Ke, Nal Kalchbrenner, Anirudh Goyal, and Yoshua Bengio · 2021
Later among the works it cites.
Causally motivated shortcut removal using auxiliary labels
Maggie Makar, Ben Packer, Dan Moldovan, Davis Blalock, Yoni Halpern, and Alexander D’Amour · 2022
Closest in time.
Robust fine-tuning of zero-shot models
Mitchell Wortsman, Gabriel Ilharco, Mike Li, Jong Wook Kim, Hannaneh Hajishirzi, Ali Farhadi, Hongseok Namkoong, and Ludwig Schmidt · 2022
Closest in time.