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
Robust Combination of Distributed Gradients Under Adversari..:
, In:
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
,
Kim, Kwang In
- p. 254-263 , 2022
Link:
https://doi.org/10.1109/CVPR52688.2022.00035
RT T1
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
: T1
Robust Combination of Distributed Gradients Under Adversarial Perturbations
UL https://suche.suub.uni-bremen.de/peid=ieee-9878831&Exemplar=1&LAN=DE A1 Kim, Kwang In YR 2022 SN 2575-7075 K1 Manifolds K1 Computer aided instruction K1 Privacy K1 Machine learning algorithms K1 Distance learning K1 Perturbation methods K1 Benchmark testing K1 Machine learning; Adversarial attack and defense; Privacy and federated learning SP 254 OP 263 LK http://dx.doi.org/https://doi.org/10.1109/CVPR52688.2022.00035 DO https://doi.org/10.1109/CVPR52688.2022.00035 SF ELIB - SuUB Bremen
Export
RefWorks (nur Desktop-Version!)
Flow
(Zuerst in
Flow
einloggen, dann importieren)