I agree that this site is using cookies. You can find further informations
here
.
X
Login
Merkliste (
0
)
Home
About us
Home About us
Our history
Profile
Press & public relations
Friends
The library in figures
Exhibitions
Projects
Training, internships, careers
Films
Services & Information
Home Services & Information
Lending and interlibrary loans
Returns and renewals
Training and library tours
My Account
Library cards
New to the library?
Download Information
Opening hours
Learning spaces
PC, WLAN, copy, scan and print
Catalogs and collections
Home Catalogs and Collections
Rare books and manuscripts
Digital collections
Subject Areas
Our sites
Home Our sites
Central Library
Law Library (Juridicum)
BB Business and Economics (BB11)
BB Physics and Electrical Engineering
TB Engineering and Social Sciences
TB Economics and Nautical Sciences
TB Music
TB Art & Design
TB Bremerhaven
Contact the library
Home Contact the library
Staff Directory
Open access & publishing
Home Open access & publishing
Reference management: Citavi & RefWorks
Publishing documents
Open Access in Bremen
zur Desktop-Version
Toggle navigation
Merkliste
1 Ergebnisse
1
Multi-Modal Machine Learning for Navigating Noisy Objective..:
, In:
2023 International Conference on Machine Learning and Applications (ICMLA)
,
Shirazi, Majid
;
Safronov, Georgii
;
Rizk, Amr
- p. 1875-1882 , 2023
Link:
https://doi.org/10.1109/ICMLA58977.2023.00284
RT T1
2023 International Conference on Machine Learning and Applications (ICMLA)
: T1
Multi-Modal Machine Learning for Navigating Noisy Objectives of Automotive Manufacturing Quality Inspection
UL https://suche.suub.uni-bremen.de/peid=ieee-10460049&Exemplar=1&LAN=DE A1 Shirazi, Majid A1 Safronov, Georgii A1 Rizk, Amr YR 2023 SN 1946-0759 K1 Training K1 Automation K1 Navigation K1 Pipelines K1 Machine learning K1 Inspection K1 Programming K1 Multi modal K1 Industrial ML SP 1875 OP 1882 LK http://dx.doi.org/https://doi.org/10.1109/ICMLA58977.2023.00284 DO https://doi.org/10.1109/ICMLA58977.2023.00284 SF ELIB - SuUB Bremen
Export
RefWorks (nur Desktop-Version!)
Flow
(Zuerst in
Flow
einloggen, dann importieren)