Zobrazeno 1 - 10
of 30
pro vyhledávání: '"Lavdim Halilaj"'
Publikováno v:
International Journal of Semantic Computing. :1-23
Autonomous Driving (AD) datasets, when used in combination with deep learning techniques, have enabled significant progress on difficult AD tasks such as perception, trajectory prediction, and motion planning. These datasets represent the content of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4b73f0f00602706ea9826436cccc1288
https://doi.org/10.1142/s1793351x23600048
https://doi.org/10.1142/s1793351x23600048
Publikováno v:
2022 IEEE Fifth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e62cad79d969461cf419cb5b82522b90
https://doi.org/10.1109/aike55402.2022.00023
https://doi.org/10.1109/aike55402.2022.00023
Publikováno v:
The Semantic Web – ISWC 2022 ISBN: 9783031194320
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a68843b3e304867d81a888f317f4136e
https://doi.org/10.1007/978-3-031-19433-7_9
https://doi.org/10.1007/978-3-031-19433-7_9
Publikováno v:
Knowledge Graphs and Semantic Web ISBN: 9783031214219
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c0b7eae1f59a6ec544b589536c6b62a8
https://doi.org/10.1007/978-3-031-21422-6_2
https://doi.org/10.1007/978-3-031-21422-6_2
Autor:
Maximilian Zipfl, Felix Hertlein, Achim Rettinger, Steffen Thoma, Lavdim Halilaj, Juergen Luettin, Stefan Schmid, Cory Henson
Representing relevant information of a traffic scene and understanding its environment is crucial for the success of autonomous driving. Modeling the surrounding of an autonomous car using semantic relations, i.e., how different traffic participants
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::84214e9b103a87c725daf381b53100c9
Learned latent vector representations are key to the success of many recommender systems in recent years. However, traditional approaches like matrix factorization produce vector representations that capture global distributions of a static recommend
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::633500a9fab11fcc0f3182661536705e
https://doi.org/10.3233/ssw210046
https://doi.org/10.3233/ssw210046
Publikováno v:
SAC
Context-aware Recommender Systems (CARS) are becoming an integral part of the everyday life by providing users the ability to retrieve relevant information based on their contextual situation. To increase the predictive power considering many paramet
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e8f9a1dd596c3dda65f2e24057af16b5
https://doi.org/10.1145/3412841.3442056
https://doi.org/10.1145/3412841.3442056
Publikováno v:
The Semantic Web – ISWC 2021 ISBN: 9783030883607
ISWC
ISWC
Traditional computer vision approaches, based on neural networks (NN), are typically trained on a large amount of image data. By minimizing the cross-entropy loss between a prediction and a given class label, the NN and its visual embedding space are
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c4237db4199d2a5d1451ef7b4bbccf6b
https://doi.org/10.1007/978-3-030-88361-4_21
https://doi.org/10.1007/978-3-030-88361-4_21
Publikováno v:
VEHITS
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d85dd052d6d09c6247b6233374190fd2
https://doi.org/10.5220/0010527807200730
https://doi.org/10.5220/0010527807200730
Publikováno v:
The Semantic Web ISBN: 9783030773847
ESWC
ESWC
Making an informed and right decision poses huge challenges for drivers in day-to-day traffic situations. This task vastly depends on many subjective and objective factors, including the current driver state, her destination, personal preferences and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b6cd0ad1752da28a57942ecbda924648
https://doi.org/10.1007/978-3-030-77385-4_42
https://doi.org/10.1007/978-3-030-77385-4_42