Zobrazeno 1 - 10 of 60 pro vyhledávání: '"Achim Rettinger"'
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Akademický článek
Abgeleitete Textformate: Text und Data Mining mit urheberrechtlich geschützten Textbeständen
Autor: Christof Schöch, Frédéric Döhl, Achim Rettinger, Evelyn Gius, Peer Trilcke, Peter Leinen, Fotis Jannidis, Maria Hinzmann, Jörg Röpke
Publikováno v: Zeitschrift für digitale Geisteswissenschaften, Iss 06 (2016)
Despite the TDM exception in German copyright law, Text and Data Mining (TDM) with copyrighted texts is still subject to restrictions, including those concerning the storage, publication and follow-up use of the resulting corpora, leaving the full
Externí odkaz: https://doaj.org/article/41b7ac72ca374126a1f08eb2dcc6cedc
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4
Relation-based Motion Prediction using Traffic Scene Graphs
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
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5
Embedding Taxonomical, Situational or Sequential Knowledge Graph Context for Recommendation Tasks
Autor: Lavdim Halilaj, Jürgen Lüttin, Achim Rettinger, Simon Werner
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
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7
Querying NoSQL with Deep Learning to Answer Natural Language Questions
Autor: Hans-Peter Zorn, Achim Rettinger, Florian Wilhelm, Sebastian Blank
Publikováno v: AAAI
Almost all of today’s knowledge is stored in databases and thus can only be accessed with the help of domain specific query languages, strongly limiting the number of people which can access the data. In this work, we demonstrate an end-to-end trai
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::45e46e7054d5af982aba5bc3ebd3e34a
https://doi.org/10.1609/aaai.v33i01.33019416
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8
Learning Visual Models Using a Knowledge Graph as a Trainer
Autor: Achim Rettinger, Lavdim Halilaj, Stefan Schmid, Sebastian Monka
Publikováno v: The Semantic Web – ISWC 2021 ISBN: 9783030883607
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
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9
RETRA: Recurrent Transformers for Learning Temporally Contextualized Knowledge Graph Embeddings
Autor: Achim Rettinger, Lavdim Halilaj, Simon Werner, Jürgen Lüttin
Publikováno v: The Semantic Web ISBN: 9783030773847
ESWC
Knowledge graph embeddings (KGE) are vector representations that capture the global distributional semantics of each entity instance and relation type in a static Knowledge Graph (KG). While KGEs have the capability to embed information related to an
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::d24de6200f77ccf38ec75ef23ada9680
https://doi.org/10.1007/978-3-030-77385-4_25
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