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1 Ergebnisse
1
Boosting interpretability of non-readable deep learning for..:
, In:
Proceedings of the Thirteenth ACM International Conference on Future Energy Systems
,
Samimi, Reza
;
Alyousef, Ammar
;
Baranzini, Daniele
. - p. 434-435 , 2022
Link:
https://dl.acm.org/doi/10.1145/3538637.3538754
RT T1
Proceedings of the Thirteenth ACM International Conference on Future Energy Systems
: T1
Boosting interpretability of non-readable deep learning forecasts : the case of buildings' energy consumptions prediction
UL https://suche.suub.uni-bremen.de/peid=acm-3538754&Exemplar=1&LAN=DE A1 Samimi, Reza A1 Alyousef, Ammar A1 Baranzini, Daniele A1 De Meer, Hermann PB ACM YR 2022 K1 AI explainability K1 AI interpretability K1 energy consumption forecasting K1 time series forecasting SP 434 OP 435 LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3538637.3538754 DO https://dl.acm.org/doi/10.1145/3538637.3538754 SF ELIB - SuUB Bremen
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