Supplementary Material for "Process Data Properties Matter: Introducing Gated Convolutional Neural Networks (GCNN) and Key-Value-Predict Attention Networks (KVP) for Next Event Prediction with Deep Learning". EUDAT B2SHARE
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%0 Generic
%1 heinrich2021supplementary
%A Heinrich, Kai
%A Zschech, Patrick
%A Janiesch, Christian
%A Bonin, Markus
%D 2021
%K bpm bwljp1 myown
%T Supplementary Material for "Process Data Properties Matter: Introducing Gated Convolutional Neural Networks (GCNN) and Key-Value-Predict Attention Networks (KVP) for Next Event Prediction with Deep Learning". EUDAT B2SHARE
%U https://doi.org/10.23728/b2share.08b7ff704f724b94a61b4a6cac0fe1e0
@dataset{heinrich2021supplementary,
added-at = {2021-01-08T16:01:20.000+0100},
author = {Heinrich, Kai and Zschech, Patrick and Janiesch, Christian and Bonin, Markus},
biburl = {https://www.bibsonomy.org/bibtex/2437a1cdb8899871b278e12b02d29f192/famerlor},
interhash = {f190c119791caebcee2d061bd2c14c7e},
intrahash = {437a1cdb8899871b278e12b02d29f192},
keywords = {bpm bwljp1 myown},
timestamp = {2022-10-28T11:16:35.000+0200},
title = {Supplementary Material for "Process Data Properties Matter: Introducing Gated Convolutional Neural Networks (GCNN) and Key-Value-Predict Attention Networks (KVP) for Next Event Prediction with Deep Learning". EUDAT B2SHARE},
url = {https://doi.org/10.23728/b2share.08b7ff704f724b94a61b4a6cac0fe1e0},
year = 2021
}
