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Toward principled regularization of deep networks: From weight decay to feature contraction
KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, perception and learning, RPL.ORCID iD: 0000-0002-4266-6746
2019 (English)In: Science Robotics, Vol. 4, no 30, article id eaaw1329Article in journal (Refereed) Published
Abstract [en]

Training deep artificial neural networks for classification problems may benefit from exploiting intrinsic class similarities by way of network regularization that compensates for a drawback in the commonly used target error.

Place, publisher, year, edition, pages
2019. Vol. 4, no 30, article id eaaw1329
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Computer Science
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URN: urn:nbn:se:kth:diva-251684DOI: 10.1126/scirobotics.aaw1329ISI: 000467971300001OAI: oai:DiVA.org:kth-251684DiVA, id: diva2:1316470
Note

QC 20190611

Available from: 2019-05-17 Created: 2019-05-17 Last updated: 2019-06-11Bibliographically approved

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Publisher's full texthttps://robotics.sciencemag.org/content/4/30/eaaw1329.full?ijkey=UKAi2RNJu83aI&keytype=ref&siteid=robotics

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Maki, Atsuto
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