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Predicting process behaviour using deep learning
Author(s):
Joerg Evermann
,
Jana-Rebecca Rehse
,
Peter Fettke
,
Travis Fisher
,
Mark Carpenter
,
Jan Nordström
,
Nail Yamaleev
,
Charles A Swanson
,
Cowan
,
Adrian Dumitru
,
Kevin Dusling
,
François Gelis
,
Jamal Jalilian-Marian
,
Tuomas Lappi
,
Raju Venugopalan
,
Torbjorn Sjostrand
,
Stefan Ask
,
Jesper Christiansen
,
Richard Corke
,
Nishita Desai
,
Philip Ilten
,
Stephen Mrenna
,
Stefan Prestel
,
Christine Rasmussen
,
Peter Z. Skands
,
Simon Wicks
,
William Horowitz
,
Magdalena Djordjevic
,
Miklos Gyulassy
,
W. Melnitchouk
,
R. Ent
,
C. Keppel
,
Clark Barrett
,
Igor Shikanian
,
Cesare Tinelli
,
A. Mouraux
,
A Diukova
,
M Lee
,
R. J. Wise
,
G. D. Iannetti
Publication date:
2017
Journal:
Decision Support Systems
Read this article at
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PubMed
There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.
Related collections
Annual Reviews AI, Machine Learning, and Society
Author and article information
Journal
PubMed ID::
20932917
ScienceOpen disciplines:
Theoretical computer science
,
Computer science
,
Neural & Evolutionary computing
,
Artificial intelligence
Data availability:
ScienceOpen disciplines:
Theoretical computer science
,
Computer science
,
Neural & Evolutionary computing
,
Artificial intelligence
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