Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
DISTRIBUTED LARGE NEURAL NETWORK WITH CENTRALIZED EQUIVALENCE
KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.
KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.
KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.ORCID iD: 0000-0002-7926-5081
KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.ORCID iD: 0000-0003-2638-6047
2018 (English)In: 2018 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), IEEE, 2018, p. 2976-2980Conference paper, Published paper (Refereed)
Abstract [en]

In this article, we develop a distributed algorithm for learning a large neural network that is deep and wide. We consider a scenario where the training dataset is not available in a single processing node, but distributed among several nodes. We show that a recently proposed large neural network architecture called progressive learning network (PLN) can be trained in a distributed setup with centralized equivalence. That means we would get the same result if the data be available in a single node. Using a distributed convex optimization method called alternating-direction-method-of-multipliers (ADMM), we perform training of PLN in the distributed setup.

Place, publisher, year, edition, pages
IEEE, 2018. p. 2976-2980
Keywords [en]
Distributed learning, neural networks, data parallelism, convex optimization
National Category
Communication Systems
Identifiers
URN: urn:nbn:se:kth:diva-237152DOI: 10.1109/ICASSP.2018.8462179ISI: 000446384603029Scopus ID: 2-s2.0-85054237028OAI: oai:DiVA.org:kth-237152DiVA, id: diva2:1258546
Conference
2018 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)
Note

QC 20181025

Available from: 2018-10-25 Created: 2018-10-25 Last updated: 2019-08-20Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopusconference

Authority records BETA

Liang, XinyueJavid, Alireza M.Skoglund, MikaelChatterjee, Saikat

Search in DiVA

By author/editor
Liang, XinyueJavid, Alireza M.Skoglund, MikaelChatterjee, Saikat
By organisation
Information Science and Engineering
Communication Systems

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 324 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf