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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.
2017
2017
Deep Automated Multi-task Learning
Multi-task learning (MTL) has recently contributed to learning better representations in service of various NLP tasks. MTL aims… 
2017
2017
A Method for Identifying the Mood States of Social Network Users Based on Cyber Psychometrics
Analyzing people’s opinions, attitudes, sentiments, and emotions based on user-generated content (UGC) is feasible for… 
2016
2016
Graph feature selection for dementia diagnosis
2016
2016
Semi-supervised feature selection with exploiting shared information among multiple tasks
2013
2013
Multi-task Multiple Kernel Learning
We present a general regularization-based framework for Multi-task learning (MTL), in which the similarity between tasks can be… 
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… 
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… 
2007
2007
Cumulative Learning based on Dynamic Clustering of Hierarchical Production Rules(HPRs)
An important structuring mechanism for knowledge bases is building clusters based on the content of their knowledge objects. The… 
1990
1990
CSM: A Computational Model of Cumulative Learning
This paper presents a description and an empirical evaluation of a rule-based, cumulative learning system called CSM (classifier… 
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