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
Demystifying sparse rectified auto-encoders:
, In:
Proceedings of the Fourth Symposium on Information and Communication Technology
,
Tran, Kien
;
Le, Bac
- p. 101-107 , 2013
Link:
https://dl.acm.org/doi/10.1145/2542050.2542065
RT T1
Proceedings of the Fourth Symposium on Information and Communication Technology
: T1
Demystifying sparse rectified auto-encoders
UL https://suche.suub.uni-bremen.de/peid=acm-2542065&Exemplar=1&LAN=DE A1 Tran, Kien A1 Le, Bac PB ACM YR 2013 K1 deep learning K1 rectified linear units K1 sparse auto-encoders K1 sparse coding K1 unsupervised feature learning K1 Computing methodologies K1 Machine learning K1 Machine learning approaches K1 Neural networks K1 Theory of computation K1 Theory and algorithms for application domains K1 Machine learning theory K1 Markov decision processes K1 Artificial intelligence K1 Computer vision K1 Computer vision representations K1 Learning paradigms SP 101 OP 107 LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/2542050.2542065 DO https://dl.acm.org/doi/10.1145/2542050.2542065 SF ELIB - SuUB Bremen
Export
RefWorks (nur Desktop-Version!)
Flow
(Zuerst in
Flow
einloggen, dann importieren)