Seyed Mehran Kazemi
Seyed Mehran Kazemi
Research Scientist, Google Research
Verified email at - Homepage
Cited by
Cited by
Simple embedding for link prediction in knowledge graphs
SM Kazemi, D Poole
NeurIPS, 2018
Representation Learning for Dynamic 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
Diachronic embedding for temporal knowledge graph completion
R Goel, SM Kazemi, M Brubaker, P Poupart
AAAI, 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
RelNN: A deep neural model for relational learning
SM Kazemi, D Poole
AAAI, 2018
Relational Logistic Regression
SM Kazemi, D Buchman, K Kersting, S Natarajan, D Poole
in Proc. 14th International Conference on Principles of Knowledge …, 2014
Population size extrapolation in relational probabilistic modelling
D Poole, D Buchman, SM Kazemi, K Kersting, S Natarajan
International Conference on Scalable Uncertainty Management, 292-305, 2014
New liftable classes for first-order probabilistic inference
SM Kazemi, A Kimmig, GV Broeck, D Poole
NeurIPS, 2016
SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks
B Fatemi, LE Asri, SM Kazemi
NeurIPS, 2021
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 (5), 2089-2111, 2021
Out-of-sample representation learning for knowledge graphs
M Albooyeh, R Goel, SM Kazemi
EMNLP, 2020
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
Relational logistic regression: The directed analog of markov logic networks
SM Kazemi, D Buchman, K Kersting, S Natarajan, D Poole
Workshops at the Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014
Bridging weighted rules and graph random walks for statistical relational models
SM Kazemi, D Poole
Frontiers in Robotics and AI 5, 8, 2018
A learning algorithm for relational logistic regression: Preliminary results
B Fatemi, SM Kazemi, D Poole
Statistical Relational AI Workshop, 2016
Comparing aggregators for relational probabilistic models
SM Kazemi, B Fatemi, A Kim, Z Peng, MR Tora, X Zeng, M Dirks, D Poole
arXiv preprint arXiv:1707.07785, 2017
Elimination Ordering in Lifted First-Order Probabilistic Inference
SM Kazemi, D Poole
Association for the Advancements of Artificial Intelligence (AAAI), 2014
Stay Positive: Knowledge Graph Embedding Without Negative Sampling
A Hajimoradlou, Kazemi, S Mehran
ICML Workshop in Graph Representation Learning and Beyond, 2020
Record linkage to match customer names: A probabilistic approach
B Fatemi, SM Kazemi, D Poole
arXiv preprint arXiv:1806.10928, 2018
Domain Recursion for Lifted Inference with Existential Quantifiers
SM Kazemi, A Kimmig, GV Broeck, D Poole
Statistical Relational AI Workshop, 2017
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