Bahare Fatemi
Bahare Fatemi
Research Scientist, Google Research
Verified email at - Homepage
Cited by
Cited by
Knowledge hypergraphs: Prediction beyond binary relations
B Fatemi, P Taslakian, D Vazquez, D Poole
International Joint Conferences on Artificial Intelligence (IJCAI) 2020, 2020
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
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
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
A learning algorithm for relational logistic regression: Preliminary results
B Fatemi, SM Kazemi, D Poole
IJCAI Workshop on StarAI, 2016
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
A retrievable genetic algorithm for efficient solving of sudoku puzzles
SM Kazemi, B Fatemi
International Journal of Computer and Information Engineering 8 (5), 736-740, 2014
Knowledge hypergraph embedding meets relational algebra
B Fatemi, P Taslakian, D Vazquez, D Poole
Journal of Machine Learning Research 24 (105), 1-34, 2023
Record Linkage to Match Customer Names: A Probabilistic Approach
B Fatemi, SM Kazemi, D Poole
ICML Workshop on StarAI, 2018
Rating and generating Sudoku puzzles based on constraint satisfaction problems
B Fatemi, SM Kazemi, N Mehrasa
International Journal of Computer and Information Engineering 8 (10), 1816-1821, 2014
Talk like a graph: Encoding graphs for large language models
B Fatemi, J Halcrow, B Perozzi
arXiv preprint arXiv:2310.04560, 2023
Ugsl: A unified framework for benchmarking graph structure learning
B Fatemi, S Abu-El-Haija, A Tsitsulin, M Kazemi, D Zelle, N Bulut, ...
arXiv preprint arXiv:2308.10737, 2023
System and method for structure learning for graph neural networks
B Fatemi, SM Kazemi, L El Asri
US Patent App. 17/484,363, 2022
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
Submix: Learning to mix graph sampling heuristics
S Abu-El-Haija, JV Dillon, B Fatemi, K Axiotis, N Bulut, J Gasteiger, ...
Uncertainty in Artificial Intelligence, 1-10, 2023
Learning to Substitute Ingredients in Recipes
B Fatemi, Q Duval, R Girdhar, M Drozdzal, A Romero-Soriano
arXiv preprint arXiv:2302.07960, 2023
Finding a Record in a Database
B Fatemi
University of British Columbia, 2017
19th International Workshop on Mining and Learning with Graphs (MLG)
N Shah, S Fakhraei, D Zheng, B Fatemi, L Akoglu
Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and …, 2023
Geometric deep learning of particle motion by MAGIK
B Fatemi, J Halcrow, K Jaqaman
Nature Machine Intelligence 5 (5), 483-484, 2023
Representation learning with explicit and implicit graph structures
B Fatemi
University of British Columbia, 2023
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