| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, ... arXiv preprint arXiv:2312.11805, 2023 | 896 | 2023 |
| Simple embedding for link prediction in knowledge graphs SM Kazemi, D Poole NeurIPS, 2018 | 869 | 2018 |
| Representation learning for dynamic graphs: A survey SM Kazemi, R Goel, K Jain, I Kobyzev, A Sethi, P Forsyth, P Poupart The Journal of Machine Learning Research 21 (1), 2648-2720, 2020 | 435 | 2020 |
| Time2vec: Learning a vector representation of time SM Kazemi, R Goel, S Eghbali, J Ramanan, J Sahota, S Thakur, S Wu, ... arXiv preprint arXiv:1907.05321, 2019 | 370 | 2019 |
| Diachronic embedding for temporal knowledge graph completion R Goel, SM Kazemi, M Brubaker, P Poupart AAAI, 2020 | 341 | 2020 |
| Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context M Reid, N Savinov, D Teplyashin, D Lepikhin, T Lillicrap, J Alayrac, ... arXiv preprint arXiv:2403.05530, 2024 | 141 | 2024 |
| SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks B Fatemi, LE Asri, SM Kazemi NeurIPS, 2021 | 133 | 2021 |
| Gemini: A family of highly capable multimodal models R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, J Schalkwyk, ... arXiv preprint arXiv:2312.11805 1, 2023 | 91 | 2023 |
| Relational Logistic Regression SM Kazemi, D Buchman, K Kersting, S Natarajan, D Poole in Proc. 14th International Conference on Principles of Knowledge …, 2014 | 67 | 2014 |
| RelNN: A deep neural model for relational learning SM Kazemi, D Poole AAAI, 2018 | 65 | 2018 |
| LAMBADA: Backward Chaining for Automated Reasoning in Natural Language SM Kazemi, N Kim, D Bhatia, X Xu, D Ramachandran ACL, 2023 | 43 | 2023 |
| New liftable classes for first-order probabilistic inference SM Kazemi, A Kimmig, GV Broeck, D Poole NeurIPS, 2016 | 43 | 2016 |
| Relational representation learning for dynamic (knowledge) graphs: A survey SM Kazemi, R Goel, K Jain, I Kobyzev, A Sethi, P Forsyth, P Poupart Journal of Machine Learning Research (JMLR) 21 (70), 1-73, 2020 | 40 | 2020 |
| Population size extrapolation in relational probabilistic modelling D Poole, D Buchman, SM Kazemi, K Kersting, S Natarajan Scalable Uncertainty Management: 8th International Conference, SUM 2014 …, 2014 | 38 | 2014 |
| Out-of-sample representation learning for knowledge graphs M Albooyeh, R Goel, SM Kazemi EMNLP, 2020 | 32* | 2020 |
| Testing the General Deductive Reasoning Capacity of Large Language Models Using OOD Examples A Saparov, RY Pang, V Padmakumar, N Joshi, SM Kazemi, N Kim, H He NeurIPS, 2023 | 29 | 2023 |
| Dr. ICL: Demonstration-Retrieved In-context Learning M Luo, X Xu, Z Dai, P Pasupat, M Kazemi, C Baral, V Imbrasaite, VY Zhao FoMo Workshop, 2023 | 26 | 2023 |
| Structure learning for relational logistic regression: an ensemble approach N Ramanan, G Kunapuli, T Khot, B Fatemi, SM Kazemi, D Poole, ... Data Mining and Knowledge Discovery 35, 2089-2111, 2021 | 19 | 2021 |
| Knowledge compilation for lifted probabilistic inference: Compiling to a low-level language SM Kazemi, D Poole Fifteenth International Conference on the Principles of Knowledge …, 2016 | 19* | 2016 |
| Bridging weighted rules and graph random walks for statistical relational models SM Kazemi, D Poole Frontiers in Robotics and AI 5, 8, 2018 | 13 | 2018 |
David PooleProfessor of Computer Science, University of British Columbia在 cs.ubc.ca 的电子邮件经过验证
