| Explainable subgraph reasoning for forecasting on temporal knowledge graphs Z Han, P Chen, Y Ma, V Tresp International Conference on Learning Representations, 2020 | 146* | 2020 |
| Learning neural ordinary equations for forecasting future links on temporal knowledge graphs Z Han, Z Ding, Y Ma, Y Gu, V Tresp Proceedings of the 2021 conference on empirical methods in natural language …, 2021 | 97* | 2021 |
| Embedding models for episodic knowledge graphs Y Ma, V Tresp, EA Daxberger Journal of Web Semantics 59, 100490, 2019 | 97 | 2019 |
| Timetraveler: Reinforcement learning for temporal knowledge graph forecasting H Sun, J Zhong, Y Ma, Z Han, K He arXiv preprint arXiv:2109.04101, 2021 | 95 | 2021 |
| Graph hawkes neural network for forecasting on temporal knowledge graphs Z Han, Y Ma, Y Wang, S Günnemann, V Tresp arXiv preprint arXiv:2003.13432, 2020 | 92* | 2020 |
| Tlogic: Temporal logical rules for explainable link forecasting on temporal knowledge graphs Y Liu, Y Ma, M Hildebrandt, M Joblin, V Tresp Proceedings of the AAAI conference on artificial intelligence 36 (4), 4120-4127, 2022 | 76 | 2022 |
| Improving visual relationship detection using semantic modeling of scene descriptions S Baier, Y Ma, V Tresp The Semantic Web–ISWC 2017: 16th International Semantic Web Conference …, 2017 | 76 | 2017 |
| Dyernie: Dynamic evolution of riemannian manifold embeddings for temporal knowledge graph completion Z Han, Y Ma, P Chen, V Tresp arXiv preprint arXiv:2011.03984, 2020 | 72 | 2020 |
| Contrastive learning for recommender system Z Liu, Y Ma, Y Ouyang, Z Xiong arXiv preprint arXiv:2101.01317, 2021 | 63 | 2021 |
| Reasoning on knowledge graphs with debate dynamics M Hildebrandt, JAQ Serna, Y Ma, M Ringsquandl, M Joblin, V Tresp Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 4123-4131, 2020 | 49 | 2020 |
| Km-bart: Knowledge enhanced multimodal bart for visual commonsense generation Y Xing, Z Shi, Z Meng, G Lakemeyer, Y Ma, R Wattenhofer arXiv preprint arXiv:2101.00419, 2021 | 40 | 2021 |
| Causal inference under networked interference and intervention policy enhancement Y Ma, V Tresp International Conference on Artificial Intelligence and Statistics, 3700-3708, 2021 | 34* | 2021 |
| 3d-retr: End-to-end single and multi-view 3d reconstruction with transformers Z Shi, Z Meng, Y Xing, Y Ma, R Wattenhofer arXiv preprint arXiv:2110.08861, 2021 | 27 | 2021 |
| Holistic Representations for Memorization and Inference. Y Ma, M Hildebrandt, V Tresp, S Baier UAI, 403-413, 2018 | 21 | 2018 |
| Embedding learning for declarative memories V Tresp, Y Ma, S Baier, Y Yang The Semantic Web: 14th International Conference, ESWC 2017, Portorož …, 2017 | 21 | 2017 |
| Multi-modal contrastive pre-training for recommendation Z Liu, Y Ma, M Schubert, Y Ouyang, Z Xiong Proceedings of the 2022 International Conference on Multimedia Retrieval, 99-108, 2022 | 19 | 2022 |
| Time-dependent entity embedding is not all you need: A re-evaluation of temporal knowledge graph completion models under a unified framework Z Han, G Zhang, Y Ma, V Tresp Proceedings of the 2021 Conference on Empirical Methods in Natural Language …, 2021 | 16 | 2021 |
| The tensor memory hypothesis V Tresp, Y Ma arXiv preprint arXiv:1708.02918, 2017 | 14* | 2017 |
| A simple but powerful graph encoder for temporal knowledge graph completion Z Ding, Y Ma, B He, J Wu, Z Han, V Tresp Intelligent Systems Conference, 729-747, 2023 | 13 | 2023 |
| TempCaps: a capsule network-based embedding model for temporal knowledge graph completion G Fu, Z Meng, Z Han, Z Ding, Y Ma, M Schubert, V Tresp, R Wattenhofer Proceedings of the Sixth Workshop on Structured Prediction for NLP, 22-31, 2022 | 13 | 2022 |
Volker TrespLudwig Maximilian University of Munich / München (LMU)Verified email at dbs.ifi.lmu.de
