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
CAMEO : A Causal Transfer Learning Approach for Performa..:
, In:
Proceedings of the 2023 ACM Symposium on Cloud Computing
,
Iqbal, Md Shahriar
;
Zhong, Ziyuan
;
Ahmad, Iftakhar
.. - p. 555-571 , 2023
Link:
https://dl.acm.org/doi/10.1145/3620678.3624791
RT T1 <cTitle>Proceedings of the 2023 ACM Symposium on Cloud Computing</cTitle>: T1 <Title>CAMEO : A Causal Transfer Learning Approach for Performance Optimization of Configurable Computer Systems</Title> UL https://suche.suub.uni-bremen.de/peid=acm-3624791&Exemplar=1&LAN=DE A1 Iqbal, Md Shahriar A1 Zhong, Ziyuan A1 Ahmad, Iftakhar A1 Ray, Baishakhi A1 Jamshidi, Pooyan PB ACM YR 2023 K1 Causal Transfer Learning K1 Highly Configurable Systems K1 Performance Optimization K1 Resource Constraints K1 General and reference K1 Computing methodologies K1 Computer systems organization K1 Software and its engineering K1 Cross-computing tools and techniques K1 Machine learning SP 555 OP 571 LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3620678.3624791 DO https://dl.acm.org/doi/10.1145/3620678.3624791 SF ELIB - SuUB Bremen
Export
RefWorks (nur Desktop-Version!)
Flow
(Zuerst in
Flow
einloggen, dann importieren)
