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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… Expand
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Related topics

Related topics

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.NET FrameworkArtificial neural networkBayesian interpretation of kernel regularizationConditional random field
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Broader (1)

Machine learning

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Review
2019
Review
2019
Overview of the CLEF-2019 CheckThat! Lab: Automatic Identification and Verification of Claims. Task 1: Check-Worthiness
We present an overview of the 2nd edition of the CheckThat! Lab, part of CLEF 2019, with focus on Task 1: Check-Worthiness in… Expand
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Review
2018
Review
2018
Overview of the CLEF-2018 CheckThat! Lab on Automatic Identification and Verification of Political Claims. Task 1: Check-Worthiness
We present an overview of the CLEF-2018 CheckThat! Lab on Automatic Identification and Verification of Political Claims, with… Expand
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Highly Cited
2018
Highly Cited
2018
Multi-task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
Numerous deep learning applications benefit from multitask learning with multiple regression and classification objectives. In… Expand
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Review
2018
Review
2018
Generalization Error in Deep Learning
Deep learning models have lately shown great performance in various fields such as computer vision, speech recognition, speech… Expand
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Highly Cited
2016
Highly Cited
2016
Multi-task Sequence to Sequence Learning
Sequence to sequence learning has recently emerged as a new paradigm in supervised learning. To date, most of its applications… Expand
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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… Expand
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Highly Cited
2010
Highly Cited
2010
A Convex Formulation for Learning Task Relationships in Multi-Task Learning
Multi-task learning is a learning paradigm which seeks to improve the generalization performance of a learning task with the help… Expand
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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… Expand
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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… Expand
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Highly Cited
2005
Highly Cited
2005
Learning Multiple Tasks with Kernel Methods
We study the problem of learning many related tasks simultaneously using kernel methods and regularization. The standard single… Expand
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