English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
 
 
DownloadE-Mail
 Local TagsRelease HistoryDetailsSummary
  From Probabilistic Graphical Models to Generalized Tensor Networks for Supervised Learning

Glasser, I., Pancotti, N., & Cirac, J. I. (2020). From Probabilistic Graphical Models to Generalized Tensor Networks for Supervised Learning. IEEE Access, 8, 68169-68182. doi:10.1109/ACCESS.2020.2986279.

Item is

 

show all hide all

Basic

show hide
Item Permalink: https://hdl.handle.net/21.11116/0000-0006-5922-0 Version Permalink: https://hdl.handle.net/21.11116/0000-0008-DF47-E
Genre: Journal Article

Files

show Files
hide Files
:
5926.pdf (Publisher version), 12MB
View   Save
File Permalink:
https://hdl.handle.net/21.11116/0000-0006-5924-E
Name:
5926.pdf
Description:
-
OA-Status:
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
View
Copyright Date:
-
Copyright Info:
-
License:
https://creativecommons.org/licenses/by/4.0/

Locators

show

Creators

show
hide
 Creators:
Glasser, Ivan1, 2, Author           
Pancotti, Nicola1, 2, 3, Author           
Cirac, J. Ignacio1, 2, Author           
Affiliations:
1Theory, Max Planck Institute of Quantum Optics, Max Planck Society, ou_1445571              
2MCQST - Munich Center for Quantum Science and Technology, External Organizations, ou_3330166              
3IMPRS (International Max Planck Research School), Max Planck Institute of Quantum Optics, Max Planck Society, ou_3164443              

Content

show

Details

show
hide
Language(s): eng - English
 Dates: 2019-12-122020-03-062020-04-07
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1109/ACCESS.2020.2986279
URI: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9058650
Other: 5926
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: IEEE Access
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: New York, NY : IEEE
Pages: - Volume / Issue: 8 Sequence Number: - Start / End Page: 68169 - 68182 Identifier: ISSN: 2169-3536
CoNE: https://pure.mpg.de/cone/journals/resource/2169-3536