@article{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 = {2017-07-17T18:53:45.000+0200},
  author = {Ruder, Sebastian},
  biburl = {https://www.bibsonomy.org/bibtex/2b2beeb63c6e8075f8c544429427b9390/axel.vogler},
  description = {An Overview of Multi-Task Learning in Deep Neural Networks},
  interhash = {5d554f48acde764703134c022d27e971},
  intrahash = {b2beeb63c6e8075f8c544429427b9390},
  keywords = {deep-learning},
  note = {cite arxiv:1706.05098Comment: 14 pages, 8 figures},
  timestamp = {2017-07-17T18:53:45.000+0200},
  title = {An Overview of Multi-Task Learning in Deep Neural Networks},
  url = {http://arxiv.org/abs/1706.05098},
  year = 2017
}

{"_id":"axHdbQWQGXMqihYm6","bibbaseid":"ruder-anoverviewofmultitasklearningindeepneuralnetworks-2017","authorIDs":[],"author_short":["Ruder, S."],"bibdata":{"bibtype":"article","type":"article","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":"2017-07-17T18:53:45.000+0200","author":[{"propositions":[],"lastnames":["Ruder"],"firstnames":["Sebastian"],"suffixes":[]}],"biburl":"https://www.bibsonomy.org/bibtex/2b2beeb63c6e8075f8c544429427b9390/axel.vogler","description":"An Overview of Multi-Task Learning in Deep Neural Networks","interhash":"5d554f48acde764703134c022d27e971","intrahash":"b2beeb63c6e8075f8c544429427b9390","keywords":"deep-learning","note":"cite arxiv:1706.05098Comment: 14 pages, 8 figures","timestamp":"2017-07-17T18:53:45.000+0200","title":"An Overview of Multi-Task Learning in Deep Neural Networks","url":"http://arxiv.org/abs/1706.05098","year":"2017","bibtex":"@article{ruder2017overview,\n abstract = {Multi-task learning (MTL) has led to successes in many applications of\r\nmachine learning, from natural language processing and speech recognition to\r\ncomputer vision and drug discovery. This article aims to give a general\r\noverview of MTL, particularly in deep neural networks. It introduces the two\r\nmost common methods for MTL in Deep Learning, gives an overview of the\r\nliterature, and discusses recent advances. In particular, it seeks to help ML\r\npractitioners apply MTL by shedding light on how MTL works and providing\r\nguidelines for choosing appropriate auxiliary tasks.},\n added-at = {2017-07-17T18:53:45.000+0200},\n author = {Ruder, Sebastian},\n biburl = {https://www.bibsonomy.org/bibtex/2b2beeb63c6e8075f8c544429427b9390/axel.vogler},\n description = {An Overview of Multi-Task Learning in Deep Neural Networks},\n interhash = {5d554f48acde764703134c022d27e971},\n intrahash = {b2beeb63c6e8075f8c544429427b9390},\n keywords = {deep-learning},\n note = {cite arxiv:1706.05098Comment: 14 pages, 8 figures},\n timestamp = {2017-07-17T18:53:45.000+0200},\n title = {An Overview of Multi-Task Learning in Deep Neural Networks},\n url = {http://arxiv.org/abs/1706.05098},\n year = 2017\n}\n\n","author_short":["Ruder, S."],"key":"ruder2017overview-1-1-1-1","id":"ruder2017overview-1-1-1-1","bibbaseid":"ruder-anoverviewofmultitasklearningindeepneuralnetworks-2017","role":"author","urls":{"Paper":"http://arxiv.org/abs/1706.05098"},"keyword":["deep-learning"],"metadata":{"authorlinks":{}},"html":""},"bibtype":"article","biburl":"http://www.bibsonomy.org/bib/author/Ruder?items=1000","creationDate":"2020-11-16T12:46:27.085Z","downloads":0,"keywords":["deep-learning"],"search_terms":["overview","multi","task","learning","deep","neural","networks","ruder"],"title":"An Overview of Multi-Task Learning in Deep Neural Networks","year":2017,"dataSources":["bp3CeCu9nW5mbwJpo","JFLn7yTRgDTDaR4Am"]}