An Overview of Multi-Task Learning in Deep Neural Networks
S. Ruder. (2017)cite arxiv:1706.05098Comment: 14 pages, 8 figures.
Аннотация
Multi-task learning (MTL) has led to successes in many applications of
machine learning, from natural language processing and speech recognition to
computer vision and drug discovery. This article aims to give a general
overview of MTL, particularly in deep neural networks. It introduces the two
most common methods for MTL in Deep Learning, gives an overview of the
literature, and discusses recent advances. In particular, it seeks to help ML
practitioners apply MTL by shedding light on how MTL works and providing
guidelines for choosing appropriate auxiliary tasks.
Описание
An Overview of Multi-Task Learning in Deep Neural Networks
%0 Generic
%1 ruder2017overview
%A Ruder, Sebastian
%D 2017
%K 2017 arxiv deep-learning neural-networks
%T An Overview of Multi-Task Learning in Deep Neural Networks
%U http://arxiv.org/abs/1706.05098
%X Multi-task learning (MTL) has led to successes in many applications of
machine learning, from natural language processing and speech recognition to
computer vision and drug discovery. This article aims to give a general
overview of MTL, particularly in deep neural networks. It introduces the two
most common methods for MTL in Deep Learning, gives an overview of the
literature, and discusses recent advances. In particular, it seeks to help ML
practitioners apply MTL by shedding light on how MTL works and providing
guidelines for choosing appropriate auxiliary tasks.
@misc{ruder2017overview,
abstract = {Multi-task learning (MTL) has led to successes in many applications of
machine learning, from natural language processing and speech recognition to
computer vision and drug discovery. This article aims to give a general
overview of MTL, particularly in deep neural networks. It introduces the two
most common methods for MTL in Deep Learning, gives an overview of the
literature, and discusses recent advances. In particular, it seeks to help ML
practitioners apply MTL by shedding light on how MTL works and providing
guidelines for choosing appropriate auxiliary tasks.},
added-at = {2018-04-14T20:45:16.000+0200},
author = {Ruder, Sebastian},
biburl = {https://www.bibsonomy.org/bibtex/2bd25a9e0d1ad9f464850c0f096d2d989/achakraborty},
description = {An Overview of Multi-Task Learning in Deep Neural Networks},
interhash = {5d554f48acde764703134c022d27e971},
intrahash = {bd25a9e0d1ad9f464850c0f096d2d989},
keywords = {2017 arxiv deep-learning neural-networks},
note = {cite arxiv:1706.05098Comment: 14 pages, 8 figures},
timestamp = {2018-04-14T20:45:16.000+0200},
title = {An Overview of Multi-Task Learning in Deep Neural Networks},
url = {http://arxiv.org/abs/1706.05098},
year = 2017
}