An open-world extension to knowledge graph completion models H Shah, J Villmow, A Ulges, U Schwanecke, F Shafait Proceedings of the AAAI conference on artificial intelligence 33 (01), 3044-3051, 2019 | 74 | 2019 |
Relation specific transformations for open world knowledge graph completion H Shah, J Villmow, A Ulges Proceedings of the graph-based methods for natural language processing …, 2020 | 12 | 2020 |
Automatic keyphrase extraction using recurrent neural networks J Villmow, M Wrzalik, D Krechel Machine Learning and Data Mining in Pattern Recognition: 14th International …, 2018 | 12 | 2018 |
CONTEST: A unit test completion benchmark featuring context J Villmow, J Depoix, A Ulges Proceedings of the 1st Workshop on Natural Language Processing for …, 2021 | 7 | 2021 |
A structural transformer with relative positions in trees for code-to-sequence tasks J Villmow, A Ulges, U Schwanecke 2021 International Joint Conference on Neural Networks (IJCNN), 1-10, 2021 | 3 | 2021 |
How Well Can Masked Language Models Spot Identifiers That Violate Naming Guidelines? J Villmow, V Campos, J Petry, A Abbad-Andaloussi, A Ulges, B Weber 2023 IEEE 23rd International Working Conference on Source Code Analysis and …, 2023 | 1 | 2023 |
Addressing leakage in self-supervised contextualized code retrieval J Villmow, V Campos, A Ulges, U Schwanecke arXiv preprint arXiv:2204.11594, 2022 | 1 | 2022 |
Value Stream Repair Using Graph Structure Learning M Wrzalik, J Eversheim, J Villmow, A Ulges, D Krechel, S Spieckermann, ... International Conference on Industrial, Engineering and Other Applications …, 2023 | | 2023 |
Bidirectional Transformer Language Models for Smart Autocompletion of Source Code F Binder, J Villmow, A Ulges Gesellschaft für Informatik, Bonn, 2021 | | 2021 |