Abstract
Iman El Kodssi, Hanae Sbai, Nassima Ait Mansour
Abstract
Authors
Institutions
No ROR-resolved institution is linked to this work yet.
Provenance
crossref
Confidence 100%
unpaywall
Confidence 95%
datacite
Confidence 0%
No local reference links have been materialized yet.
No local citing links have been materialized yet.
Unresolved referenced work
2024
10.1109/comst.2015.2444095
10.1109/comst.2015.2444095
10.1002/widm.1350
10.1002/widm.1350
10.1109/jiot.2014.2306328
10.1109/jiot.2014.2306328
Unresolved referenced work
2023
10.1007/978-3-662-49851-4
10.1007/978-3-662-49851-4
10.1109/ieeestd.2023.10267858
10.1109/ieeestd.2023.10267858
10.1007/978-3-642-38697-8_17
10.1007/978-3-642-38697-8_17
10.3233/ica-2003-10205
10.3233/ica-2003-10205
10.1007/s10115-018-1214-x
10.1007/s10115-018-1214-x
10.1109/msmc.2020.3003135
10.1109/msmc.2020.3003135
10.1109/edocw49879.2020.00016
10.1109/edocw49879.2020.00016
10.1007/978-3-319-56994-9_18
10.1007/978-3-319-56994-9_18
Temporal conformance checking at runtime based on time-infused process models
2020
10.1109/icpm53251.2021.9576856
10.1109/icpm53251.2021.9576856
10.1016/j.is.2015.03.010
10.1016/j.is.2015.03.010
Framework for process discovery from sensor event data
2018
10.14778/3364324.3364332
10.14778/3364324.3364332
10.1162/neco.1997.9.8.1735
10.1162/neco.1997.9.8.1735
Semi-supervised classification with graph convolutional networks
Kept as external metadata until matched
Graph attention networks
Kept as external metadata until matched
10.1109/cvpr.2017.113
10.1109/cvpr.2017.113
An empirical evaluation of generic convolutional and recurrent networks for sequence modeling
2018
10.1007/s10994-018-5702-8
10.1007/s10994-018-5702-8
10.1007/978-3-319-59536-8_30
10.1007/978-3-319-59536-8_30
10.1007/978-3-319-93417-4_38
10.1007/978-3-319-93417-4_38
10.1016/j.scs.2020.102332
10.1016/j.scs.2020.102332
10.1109/tnnls.2020.2978386
10.1109/tnnls.2020.2978386
10.48550/arxiv.1810.04805
10.48550/arxiv.1810.04805
10.48550/arxiv.1907.11692
10.48550/arxiv.1907.11692
10.18653/v1/d19-1410
10.18653/v1/d19-1410
10.1007/978-3-031-17604-3_11
10.1007/978-3-031-17604-3_11
10.18653/v1/n16-1030
10.18653/v1/n16-1030
Language models are few-shot learners
Kept as external metadata until matched
GPT-4 technical report
2023
10.52202/068431-1800
10.52202/068431-1800
Retrieval-augmented generation for knowledge-intensive NLP tasks
Kept as external metadata until matched
10.1007/978-3-031-50974-2_34
10.1007/978-3-031-50974-2_34
10.18653/v1/2024.acl-long.810
10.18653/v1/2024.acl-long.810
10.1016/j.jcp.2018.10.045
10.1016/j.jcp.2018.10.045
10.1007/978-3-030-05318-5
10.1007/978-3-030-05318-5 · doi-reference
10.3233/fi-2020-1946
10.3233/fi-2020-1946 · doi-reference
10.1016/j.sigpro.2019.107299
10.1016/j.sigpro.2019.107299 · doi-reference
10.1109/tbdata.2019.2921572
10.1109/tbdata.2019.2921572 · doi-reference
10.1016/j.artmed.2017.07.002
10.1016/j.artmed.2017.07.002 · doi-reference
10.18653/v1/2020.emnlp-demos.6
10.18653/v1/2020.emnlp-demos.6 · doi-reference
10.1145/2993422.2993577
10.1145/2993422.2993577 · doi-reference
10.1007/978-3-642-15618-2_16
10.1007/978-3-642-15618-2_16 · doi-reference
10.1016/b978-0-12-799959-3.00021-5
10.1016/b978-0-12-799959-3.00021-5 · doi-reference
10.1007/s13740-014-0038-4
10.1007/s13740-014-0038-4 · doi-reference
10.1016/j.websem.2015.12.004
10.1016/j.websem.2015.12.004 · doi-reference
10.1016/j.cma.2019.112623
10.1016/j.cma.2019.112623 · doi-reference
10.1016/j.jcp.2020.109913
10.1016/j.jcp.2020.109913 · doi-reference
10.1038/s42254-021-00314-5
10.1038/s42254-021-00314-5 · doi-reference
10.1109/ic3tes62412.2024.10877481
10.1109/ic3tes62412.2024.10877481 · doi-reference
10.1109/iccv.2015.123
10.1109/iccv.2015.123 · doi-reference