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HATS : A Hierarchical Sequence-Attention Framework for I..:
, In:
Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
,
Meng, Changping
;
Yang, Jiasen
;
Ribeiro, Bruno
. - p. 783-792 , 2019
Link:
https://dl.acm.org/doi/10.1145/3292500.3330876
RT T1
Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
: T1
HATS : A Hierarchical Sequence-Attention Framework for Inductive Set-of-Sets Embeddings
UL https://suche.suub.uni-bremen.de/peid=acm-3330876&Exemplar=1&LAN=DE A1 Meng, Changping A1 Yang, Jiasen A1 Ribeiro, Bruno A1 Neville, Jennifer PB ACM YR 2019 K1 hierarchical attention K1 inductive embedding K1 long-short term memory network K1 permutation-invariance K1 sequence model K1 set-of-sets K1 Computing methodologies K1 Machine learning K1 Learning paradigms K1 Supervised learning K1 Machine learning approaches K1 Neural networks SP 783 OP 792 LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3292500.3330876 DO https://dl.acm.org/doi/10.1145/3292500.3330876 SF ELIB - SuUB Bremen
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