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
Advances in neural information processing systems, 4284-4295, 2018
Relational Logistic Regression
SM Kazemi, D Buchman, K Kersting, S Natarajan, D Poole
in Proc. 14th International Conference on Principles of Knowledge …, 2014
RelNN: A deep neural model for relational learning
SM Kazemi, D Poole
Thirty-Second AAAI Conference on Artificial Intelligence, 2018
New liftable classes for first-order probabilistic inference
SM Kazemi, A Kimmig, G Van den Broeck, D Poole
Advances in Neural Information Processing Systems, 3117-3125, 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
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
A learning algorithm for relational logistic regression: Preliminary results
B Fatemi, SM Kazemi, D Poole
Statistical Relational AI Workshop, 2016
Relational representation learning for dynamic (knowledge) graphs: A survey
SM Kazemi, R Goel, K Jain, I Kobyzev, A Sethi, P Forsyth, P Poupart
arXiv preprint arXiv:1905.11485, 2019
Elimination Ordering in Lifted First-Order Probabilistic Inference
SM Kazemi, D Poole
Association for the Advancements of Artificial Intelligence (AAAI), 2014
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
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 Retrievable Genetic Algorithm for Efficient Solving of Sudoku Puzzles
SM Kazemi, B Fatemi
International Journal of Computer, Information Science and Engineering 8, 2014
Structure learning for relational logistic regression: An ensemble approach
N Ramanan, G Kunapuli, T Khot, B Fatemi, SM Kazemi, D Poole, ...
Sixteenth International Conference on Principles of Knowledge Representation …, 2018
Why is Compiling Lifted Inference into a Low-Level Language so Effective?
SM Kazemi, D Poole
arXiv preprint arXiv:1606.04512, 2016
Diachronic embedding for temporal knowledge graph completion
R Goel, SM Kazemi, M Brubaker, P Poupart
Thirty-Fourth AAAI Conference on Artificial Intelligence, 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
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
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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