Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Gereon Hinz"'
Publikováno v:
Information, Vol 11, Iss 11, p 525 (2020)
Recent advancements in deep neural networks for graph-structured data have led to state-of-the-art performance in recommender system benchmarks. Adapting these methods to pharmacy product cross-selling recommendation tasks with a million products and
Externí odkaz:
https://doaj.org/article/f848ffb5730e47abb540c4130cf98d68
Autor:
Guang Chen, Hu Cao, Muhammad Aafaque, Jieneng Chen, Canbo Ye, Florian Röhrbein, Jörg Conradt, Kai Chen, Zhenshan Bing, Xingbo Liu, Gereon Hinz, Walter Stechele, Alois Knoll
Publikováno v:
Journal of Advanced Transportation, Vol 2018 (2018)
Neuromorphic vision sensor is a new passive sensing modality and a frameless sensor with a number of advantages over traditional cameras. Instead of wastefully sending entire images at fixed frame rate, neuromorphic vision sensor only transmits the l
Externí odkaz:
https://doaj.org/article/80077ea73881438ba1caf282b4011c68
Autor:
Patrik Omland, Alessio Netti, Yang Peng, Andrea Baldovin, Michael Paulitsch, Gustavo Espinosa, Jorge Parra, Gereon Hinz, Alois Knoll
Publikováno v:
IEEE Transactions on Parallel and Distributed Systems. 34:995-1006
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4d7e80788b42e6631924754b3d09f88a
https://doi.org/10.1109/tpds.2023.3237777
https://doi.org/10.1109/tpds.2023.3237777
Publikováno v:
2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC).
Driving through complex urban environments is a challenging task for autonomous vehicles (AVs), as they must safely reach their mission goal, and react properly to traffic participants while obeying traffic rules. Deep reinforcement learning (DRL) is
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::beb9912e6fa362f206735b3470429b8d
https://doi.org/10.1109/itsc55140.2022.9922164
https://doi.org/10.1109/itsc55140.2022.9922164
Publikováno v:
2022 IEEE 5th International Conference on Industrial Cyber-Physical Systems (ICPS).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e27913baf9f02a15820bab66c71daa7c
https://doi.org/10.1109/icps51978.2022.9816927
https://doi.org/10.1109/icps51978.2022.9816927
Autor:
Syed Qutub, Florian Geissler, Yang Peng, Ralf Gräfe, Michael Paulitsch, Gereon Hinz, Alois Knoll
Publikováno v:
Safecomp 2022
Lecture Notes in Computer Science
Lecture Notes in Computer Science-Computer Safety, Reliability, and Security
Lecture Notes in Computer Science ISBN: 9783031148347
Lecture Notes in Computer Science
Lecture Notes in Computer Science-Computer Safety, Reliability, and Security
Lecture Notes in Computer Science ISBN: 9783031148347
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::40e7a5df5d781d950460c88f0471fcc5
Driving autonomous vehicles safely through a complex urban environment remains a difficult task. The sensor limitations, as well as the various occlusions in the urban environment caused by static and dynamic objects, make the decision-making task ev
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::20bbff38ddb27a17d589eaaf8ac37236
https://mediatum.ub.tum.de/doc/1688107/document.pdf
https://mediatum.ub.tum.de/doc/1688107/document.pdf
Driving through dense urban environments is difficult for autonomous vehicles because they must reason about the unknown intentions of a large number of road users while also dealing with a variety of uncertain information, such as sensor noise and i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2a47e4db955833bb769309e79d9056c6
https://mediatum.ub.tum.de/1688109
https://mediatum.ub.tum.de/1688109
Autor:
Sakshi Goyal, Chen Yiqiang, Gereon Hinz, Alois Knoll, Ke Pei, Tetiana Lytvynenko, Franz Hell, Feng Liu
Publikováno v:
CSCS
Deep neural networks are at the heart of safety-critical applications such as autonomous driving. Distributional shift is a typical problem in predictive modeling, when the feature distribution of inputs and outputs varies between the training and te
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d2d1b9022859c51a0181726bb24de91d
https://doi.org/10.1145/3488904.3493382
https://doi.org/10.1145/3488904.3493382