Multi-task learning

Multi-task learning (MTL) is an approach to machine learning that learns a problem together with other related problems at the same time, using a… (More)
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.NET FrameworkArtificial neural networkBayesian interpretation of kernel regularizationConditional random field
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Machine learning

Papers overview

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Review
2017
Review
2017
An Overview of Multi-Task Learning in Deep Neural Networks
Multi-task learning (MTL) has led to successes in many applications of machine learning, from natural language processing and… (More)
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Highly Cited
2016
Highly Cited
2016
Cross-Stitch Networks for Multi-task Learning
Multi-task learning in Convolutional Networks has displayed remarkable success in the field of recognition. This success can be… (More)
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Highly Cited
2015
Highly Cited
2015
Multi-Task Learning for Multiple Language Translation
In this paper, we investigate the problem of learning a machine translation model that can simultaneously translate sentences… (More)
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Highly Cited
2014
Highly Cited
2014
Facial Landmark Detection by Deep Multi-task Learning
Facial landmark detection has long been impeded by the problems of occlusion and pose variation. Instead of treating the… (More)
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Highly Cited
2012
Highly Cited
2012
Learning Task Grouping and Overlap in Multi-task Learning
In the paradigm of multi-task learning, multiple related prediction tasks are learned jointly, sharing information across the… (More)
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Highly Cited
2010
Highly Cited
2010
A Dirty Model for Multi-task Learning
We consider multi-task learning in the setting of multiple l inear regression, and where some relevant features could be shared… (More)
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Highly Cited
2008
Highly Cited
2008
Clustered Multi-Task Learning: A Convex Formulation
In multi-task learning several related tasks are considered simultaneously, with the hope that by an appropriate sharing of… (More)
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Highly Cited
2008
Highly Cited
2008
Multi-task learning for HIV therapy screening
We address the problem of learning classifiers for a large number of tasks. We derive a solution that produces resampling weights… (More)
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Highly Cited
2007
Highly Cited
2007
Multi-Task Learning for Classification with Dirichlet Process Priors
Consider the problem of learning logistic-regression models for multiple classification tasks, where the training data set for… (More)
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Highly Cited
2007
Highly Cited
2007
Multi-task Gaussian Process Prediction
In this paper we investigate multi-task learning in the context of Gaussian Processes (GP). We propose a model that learns a… (More)
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