%0 Conference Paper %1 conf/iiwas/LiRB22 %A Li, Chaofan %A Riedel, Till %A Beigl, Michael %B iiWAS %D 2022 %E Pardede, Eric %E Haghighi, Pari Delir %E Khalil, Ismail %E Kotsis, Gabriele %I Springer %K dblp %P 252-266 %T Neural Kernel Network Deep Kernel Learning for Predicting Particulate Matter from Heterogeneous Sensors with Uncertainty. %U http://dblp.uni-trier.de/db/conf/iiwas/iiwas2022.html#LiRB22 %V 13635 %@ 978-3-031-21047-1

@inproceedings{conf/iiwas/LiRB22, added-at = {2024-02-05T00:00:00.000+0100}, author = {Li, Chaofan and Riedel, Till and Beigl, Michael}, biburl = {https://www.bibsonomy.org/bibtex/2aa6c6d6ebb925091ad229b75e70d4ee4/dblp}, booktitle = {iiWAS}, crossref = {conf/iiwas/2022}, editor = {Pardede, Eric and Haghighi, Pari Delir and Khalil, Ismail and Kotsis, Gabriele}, ee = {https://doi.org/10.1007/978-3-031-21047-1_22}, interhash = {aece3814c8914379b536a5cd1e69705b}, intrahash = {aa6c6d6ebb925091ad229b75e70d4ee4}, isbn = {978-3-031-21047-1}, keywords = {dblp}, pages = {252-266}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, timestamp = {2024-04-10T01:38:24.000+0200}, title = {Neural Kernel Network Deep Kernel Learning for Predicting Particulate Matter from Heterogeneous Sensors with Uncertainty.}, url = {http://dblp.uni-trier.de/db/conf/iiwas/iiwas2022.html#LiRB22}, volume = 13635, year = 2022 }