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Hi editors of "Multi-task learning",

As a project for a course I have completed a major edit for this article. Because of the assessment requirements for the course, the edit was completed "offline".

"Multi-task learning" was selected for improvement since it did not contain any mathematical details and much of the text was copy-pasted from various article abstracts and sources around the internet. I attempted to give better attribution, although I left some text unedited. In particular some of Kilian Weinberger's wording remains particularly since I wasn't sure if in fact he was the one who posted it here.

I tried to edit all sections for clarity, relevance, and brevity. I also added mathematical details for RKHSvv taken from Carlo Ciliberto's recent paper, which provides somewhat of an overview of this take on the subject. I suspect the level of detail is too much, as I had the instruction staff of my course as a primary audience in mind.

You can view the newly edited article here. https://en.wikipedia.org/w/index.php?title=Draft:Multi-task_learning&redirect=no Unless there are major objections, I will move the text over here say around 1st Jan.

Thanks Mattheworlando (talk) 20:27, 18 December 2015 (UTC)[reply]

[edit]

Hello fellow Wikipedians,

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Cheers.—InternetArchiveBot (Report bug) 10:49, 12 January 2018 (UTC)[reply]

Needs an Overhaul

[edit]

I think this page needs a bit of an overhaul/update. It seems oddly focused on mathematical, shallow learning, and robotics applications for a technique that is now used extensively in the machine learning community across a wide range of disciplines (especially deep learning for NLP).

A lot of the existing content should probably move under a "Theoretical underpinnings for why multitask learning works, conceptually" section.

Sebastian Ruder's survey of multitask learning has been quite widely cited so it might be worth using that as a guide for the structure:

  • Types of parameter sharing (hard vs soft vs hierarchical)
  • Theoretical basis for MTL (include much of the existing content here)
  • MTL in non-neural models (include more of the existing shallow-learning discussion here)
  • Deep learning approaches - highlights of DL research, maybe split by field
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