Process data properties matter: Introducing gated convolutional neural networks (GCNN) and key-value-predict attention networks (KVP) for next event prediction with deep learning.
Bitte melden Sie sich an um selbst Rezensionen oder Kommentare zu erstellen.
Zitieren Sie diese Publikation
Mehr Zitationsstile
- bitte auswählen -
%0 Journal Article
%1 journals/dss/HeinrichZJB21
%A Heinrich, Kai
%A Zschech, Patrick
%A Janiesch, Christian
%A Bonin, Markus
%D 2021
%J Decis. Support Syst.
%K dblp
%P 113494
%T Process data properties matter: Introducing gated convolutional neural networks (GCNN) and key-value-predict attention networks (KVP) for next event prediction with deep learning.
%U http://dblp.uni-trier.de/db/journals/dss/dss143.html#HeinrichZJB21
%V 143
@article{journals/dss/HeinrichZJB21,
added-at = {2021-07-25T00:00:00.000+0200},
author = {Heinrich, Kai and Zschech, Patrick and Janiesch, Christian and Bonin, Markus},
biburl = {https://www.bibsonomy.org/bibtex/2a7117e9cd4ff4af2440bd662f88cdaa2/dblp},
ee = {https://doi.org/10.1016/j.dss.2021.113494},
interhash = {bffcea0f2bc174cdf4cdac7d28f129ba},
intrahash = {a7117e9cd4ff4af2440bd662f88cdaa2},
journal = {Decis. Support Syst.},
keywords = {dblp},
pages = 113494,
timestamp = {2024-04-08T09:44:44.000+0200},
title = {Process data properties matter: Introducing gated convolutional neural networks (GCNN) and key-value-predict attention networks (KVP) for next event prediction with deep learning.},
url = {http://dblp.uni-trier.de/db/journals/dss/dss143.html#HeinrichZJB21},
volume = 143,
year = 2021
}
