Seyed Mehran Kazemi
Seyed Mehran Kazemi
Machine Learning Researcher at Borealis AI
Verified email at - Homepage
Cited by
Cited by
Simple embedding for link prediction in knowledge graphs
SM Kazemi, D Poole
NeurIPS, 2018
Relational Logistic Regression
SM Kazemi, D Buchman, K Kersting, S Natarajan, D Poole
in Proc. 14th International Conference on Principles of Knowledge …, 2014
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
RelNN: A deep neural model for relational learning
SM Kazemi, D Poole
AAAI, 2018
Diachronic embedding for temporal knowledge graph completion
R Goel, SM Kazemi, M Brubaker, P Poupart
AAAI, 2020
New liftable classes for first-order probabilistic inference
SM Kazemi, A Kimmig, GV Broeck, D Poole
NeurIPS, 2016
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
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
Knowledge Compilation for Lifted Probabilistic Inference: Compiling to a Low-Level Language.
SM Kazemi, D Poole
KR, 561-564, 2016
Structure learning for relational logistic regression: An ensemble approach
N Ramanan, G Kunapuli, T Khot, B Fatemi, SM Kazemi, D Poole, ...
arXiv preprint arXiv:1808.02123, 2018
A learning algorithm for relational logistic regression: Preliminary results
B Fatemi, SM Kazemi, D Poole
Statistical Relational AI Workshop, 2016
Bridging weighted rules and graph random walks for statistical relational models
SM Kazemi, D Poole
Frontiers in Robotics and AI 5, 8, 2018
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
Relational Logistic Regression: The Directed Analog of Markov Logic Networks.
SM Kazemi, D Buchman, K Kersting, S Natarajan, D Poole
AAAI Workshop: Statistical Relational Artificial Intelligence, 2014
Elimination Ordering in Lifted First-Order Probabilistic Inference
SM Kazemi, D Poole
Association for the Advancements of Artificial Intelligence (AAAI), 2014
A Retrievable Genetic Algorithm for Efficient Solving of Sudoku Puzzles
SM Kazemi, B Fatemi
International Journal of Computer, Information Science and Engineering 8, 2014
Why is Compiling Lifted Inference into a Low-Level Language so Effective?
SM Kazemi, D Poole
arXiv preprint arXiv:1606.04512, 2016
Record linkage to match customer names: A probabilistic approach
B Fatemi, SM Kazemi, D Poole
arXiv preprint arXiv:1806.10928, 2018
Representing and learning relations and properties under uncertainty
SM Kazemi
University of British Columbia, 2018
Rating and generating Sudoku puzzles based on constraint satisfaction problems
B Fatemi, SM Kazemi, N Mehrasa
Int. J. Comput. Inf. Eng 8, 1811-1816, 2014
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