Multitask Learning

@article{Caruana2004MultitaskL,
  title={Multitask Learning},
  author={Rich Caruana},
  journal={Machine Learning},
  year={2004},
  volume={28},
  pages={41-75}
}
  • R. Caruana
  • Published 1 July 1997
  • Computer Science
  • Machine Learning
Multitask Learning is an approach to inductive transfer that improves generalization by using the domain information contained in the training signals of related tasks as an inductive bias. [] Key Result Because multitask learning works, can be applied to many different kinds of domains, and can be used with different learning algorithms, we conjecture there will be many opportunities for its use on real-world problems.
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TLDR
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TLDR
It is proposed that the concept of shared subspace provides a useful framework for the experimental study of human multitask and transfer learning, and the roles of subspaces are highlighted, showing how they could act as a learning boost if shared, and be detrimental if not.
A Regularization Approach to Learning Task Relationships in Multitask Learning
TLDR
A regularization approach to learning the relationships between tasks in multitask learning that can also describe negative task correlation and identify outlier tasks based on the same underlying principle is proposed.
Representation Learning via Semi-Supervised Autoencoder for Multi-task Learning
TLDR
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Navigating the Trade-Off between Multi-Task Learning and Learning to Multitask in Deep Neural Networks
TLDR
This work builds on previous work involving shallow networks and simple task settings suggesting that there is a trade-off between multi-task learning and multitasking, mediated by the use of shared versus separated representations, and shows that the same tension arises in deep networks.
Efficient Multitask Feature and Relationship Learning
TLDR
This paper proposes an efficient coordinate-wise minimization algorithm that has a closed form solution for each block subproblem, and provides a nonlinear extension that is able to achieve better generalization than existing methods.
Bioinspired Architecture Selection for Multitask Learning
TLDR
A new method to completely design MTL architectures, by including the selection of the most helpful subtasks for the learning of the main task, and the optimal network connections, which realizes a complete design of the MTL schemes.
and Adaptive Methods for Multitask Learning
TLDR
The primary focus of this thesis is to scale the multitask and lifelong learning to practical applications where both the tasks and the examples of the tasks arrive in an online fashion.
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