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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… 
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Related topics

Related topics

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.NET FrameworkArtificial neural networkBayesian interpretation of kernel regularizationConditional random field

Broader (1)

Machine learning

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2016
2016
Context-aware mathematical expression recognition: An end-to-end framework and a benchmark
In this paper we propose a novel end-to-end framework for mathematical expression (ME) recognition. The method uses a… 
2016
2016
Semi-supervised feature selection with exploiting shared information among multiple tasks
2016
2016
Highlighting root notes in chord recognition using cepstral features and multi-task learning
A musical chord is usually described by its root note and the chord type. While a substantial amount of work has been done in the… 
2013
2013
Transfer learning using a nonparametric sparse topic model
2012
2012
An annotation rule extraction algorithm for image retrieval
2011
2011
Multi-Task Learning for Spoken Language Understanding with Shared Slots
This paper addresses the problem of learning multiple spoken language understanding (SLU) tasks that have overlapping sets of… 
2010
2010
Multi-task Learning for one-class classification
In this paper, we address the problem of one-class classification. Taking into account the fact that in some applications, the… 
2010
2010
Multi-Task Sparse Discriminant Analysis (MtSDA) with Overlapping Categories
Multi-task learning aims at combining information across tasks to boost prediction performance, especially when the number of… 
2008
2008
Learning Coordinate Gradients with Multi-Task Kernels
Coordinate gradient learning is motivated by the problem of variable selection and determining variable covariation. In this… 
2008
2008
MTForest: Ensemble Decision Trees based on Multi-Task Learning
Many ensemble methods, such as Bagging, Boosting, Random Forest, etc, have been proposed and widely used in real world… 
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