| SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks B Fatemi, LE Asri, SM Kazemi Advances in Neural Information Processing Systems (NeurIPS) 2021, 2021 | 188* | 2021 |
| Talk like a graph: Encoding graphs for large language models B Fatemi, J Halcrow, B Perozzi International Conference on Learning Representations (ICLR) 2024, 2024 | 166 | 2024 |
| Knowledge hypergraphs: Prediction beyond binary relations B Fatemi, P Taslakian, D Vazquez, D Poole International Joint Conferences on Artificial Intelligence (IJCAI) 2020, 2020 | 136 | 2020 |
| Let your graph do the talking: Encoding structured data for llms B Perozzi, B Fatemi, D Zelle, A Tsitsulin, M Kazemi, R Al-Rfou, J Halcrow arXiv preprint arXiv:2402.05862, 2024 | 71 | 2024 |
| Improved knowledge graph embedding using background taxonomic information B Fatemi, S Ravanbakhsh, D Poole Proceedings of the AAAI conference on artificial intelligence 33 (01), 3526-3533, 2019 | 41 | 2019 |
| Don't Forget to Connect! Improving RAG with Graph-based Reranking J Dong, B Fatemi, B Perozzi, LF Yang, A Tsitsulin arXiv preprint arXiv:2405.18414, 2024 | 21 | 2024 |
| Structure learning for relational logistic regression: An ensemble approach N Ramanan, G Kunapuli, T Khot, B Fatemi, SM Kazemi, D Poole, ... Proceedings of the Sixteenth International Conference on Principles of …, 2018 | 21 | 2018 |
| Understanding transformer reasoning capabilities via graph algorithms C Sanford, B Fatemi, E Hall, A Tsitsulin, M Kazemi, J Halcrow, B Perozzi, ... Advances in Neural Information Processing Systems 37, 78320-78370, 2024 | 19 | 2024 |
| Test of time: A benchmark for evaluating llms on temporal reasoning B Fatemi, M Kazemi, A Tsitsulin, K Malkan, J Yim, J Palowitch, S Seo, ... arXiv preprint arXiv:2406.09170, 2024 | 16 | 2024 |
| Text-space graph foundation models: Comprehensive benchmarks and new insights Z Chen, H Mao, J Liu, Y Song, B Li, W Jin, B Fatemi, A Tsitsulin, B Perozzi, ... arXiv preprint arXiv:2406.10727, 2024 | 15 | 2024 |
| TpuGraphs: A Performance Prediction Dataset on Large Tensor Computational Graphs PM Phothilimthana, S Abu-El-Haija, K Cao, B Fatemi, C Mendis, B Perozzi Advances in Neural Information Processing Systems (NeurIPS) 2023, 2023 | 15 | 2023 |
| Knowledge hypergraph embedding meets relational algebra B Fatemi, P Taslakian, D Vazquez, D Poole Journal of Machine Learning Research 24 (105), 1-34, 2023 | 15 | 2023 |
| Knowledge hypergraphs: Extending knowledge graphs beyond binary relations B Fatemi, P Taslakian, D Vázquez, D Poole arXiv preprint arXiv:1906.00137, 2019 | 15 | 2019 |
| Learning to substitute ingredients in recipes B Fatemi, Q Duval, R Girdhar, M Drozdzal, A Romero-Soriano arXiv preprint arXiv:2302.07960, 2023 | 12 | 2023 |
| A learning algorithm for relational logistic regression: Preliminary results B Fatemi, SM Kazemi, D Poole IJCAI Workshop on StarAI, 2016 | 12 | 2016 |
| Michelangelo: Long context evaluations beyond haystacks via latent structure queries K Vodrahalli, S Ontanon, N Tripuraneni, K Xu, S Jain, R Shivanna, J Hui, ... arXiv preprint arXiv:2409.12640, 2024 | 11* | 2024 |
| System and method for structure learning for graph neural networks B Fatemi, SM Kazemi, L El Asri US Patent App. 17/484,363, 2022 | 10 | 2022 |
| Remi: A dataset for reasoning with multiple images M Kazemi, N Dikkala, A Anand, P Devic, I Dasgupta, F Liu, B Fatemi, ... Advances in Neural Information Processing Systems 37, 60088-60109, 2024 | 9 | 2024 |
| Record Linkage to Match Customer Names: A Probabilistic Approach B Fatemi, SM Kazemi, D Poole ICML Workshop on StarAI, 2018 | 9 | 2018 |
| Comparing aggregators for relational probabilistic models SM Kazemi, B Fatemi, A Kim, Z Peng, MR Tora, X Zeng, M Dirks, D Poole UAI Workshop on StarAI, 2017 | 9* | 2017 |
Mehran KazemiStaff Research Scientist, Google DeepMindVerified email at google.com