Publications

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Author [ Title(Desc)] Type Year
C
Poole, D. & Crowley, M., 2013. Cyclic causal models with discrete variables: Markov chain equilibrium semantics and sample ordering. In IJCAI International Joint Conference on Artificial Intelligence. Beijing, China, pp. 1060–1068. Available at: http://dl.acm.org/citation.cfm?id=2540281.
Poole, D. & Crowley, M., 2013. Cyclic causal models with discrete variables: Markov chain equilibrium semantics and sample ordering. In IJCAI International Joint Conference on Artificial Intelligence. Beijing, China, pp. 1060–1068. Available at: http://dl.acm.org/citation.cfm?id=2540281.
D
Subramanian, S.G. et al., 2018. Decision Assist For Self-Driving Cars. In 31st Canadian Conference on Artificial Intelligence, Candian AI 2018. Toronto, Ontario, Canada: Springer, pp. 381-387. Available at: https://link.springer.com/chapter/10.1007%2F978-3-319-89656-4_44.
SelfDrivingAssist_3.pdf
Bhalla, S., Subramanian, S.Ganapathi & Crowley, M., 2020. Deep Multi Agent Reinforcement Learning for Autonomous Driving. In Canadian Conference on Artificial Intelligence. Spring, Lecture Notes in Artificial Intelligence, p. 17.
deep_multi_agent_reinforcement_learning_for_autonomous_driving-full.pdf
Ghojogh, B. & Salehkaleybar, S., 2019. Distributed Voting in Beep Model. arXiv preprint arXiv:1910.09882.
E
Ghojogh, B., Karray, F. & Crowley, M., 2019. Eigenvalue and Generalized Eigenvalue Problems: Tutorial. ArXiv Preprint arXiv:1903.11240.
Crowley, M., 2011. Equilibrium Policy Gradients for Spatiotemporal Planning. University of British Columbia. Available at: http://hdl.handle.net/2429/38971.
Crowley, M., 2004. Evaluating Influence Diagrams. Unpublished Working Paper.
F
Ghojogh, B. et al., 2019. Feature selection and feature extraction in pattern analysis: A literature review. arXiv preprint arXiv:1905.02845.
Ghojogh, B., Karray, F. & Crowley, M., 2019. Fisher and kernel fisher discriminant analysis: Tutorial. arXiv preprint arXiv:1906.09436.
Ghojogh, B. et al., 2020. Fisher Discriminant Triplet and Contrastive Losses for Training Siamese Networks. In IEEE International Joint Conference on Neural Networks (IJCNN). Glasgow, UK: IEEE.
Ghojogh, B. et al., 2019. Fitting A Mixture Distribution to Data: Tutorial. ArXiv preprint. arXiv:1901.06708.
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Ghojogh, B., Karray, F. & Crowley, M., 2020. Generalized Subspace Learning by Roweis Discriminant Analysis. In International Conference on Image Analysis and Recognition. Póvoa de Varzim, Portugal (virtual): Springer. Available at: http://arxiv.org/abs/1910.05437.
2021-smc-ghojogh-generative_locally_linear_embedding.pdf
2021-softimp-ghojogh-generative.pdf
H
Ghojogh, B., Karray, F. & Crowley, M., 2019. Hidden Markov Model: Tutorial.
I
Ghojogh, B., Karray, F. & Crowley, M., 2019. Image Structure Subspace Learning Using Structural Similarity Index. In International Conference on Image Analysis and Recognition (ICIAR-19). Waterloo, Canada: Springer, Cham, pp. 33–44.
Ghojogh, B. & Crowley, M., 2019. Instance Ranking and Numerosity Reduction Using Matrix Decompositionand Subspace Learning. In Canadian Conference on Artificial Intelligence. Kingston, ON, Canada: Springer’s Lecture Notes in Artificial Intelligence., p. 12.
Akgun, S.Alperen et al., 2021. Integrating Affective Expressions into the Search and Rescue Context in order to Improve Non-Verbal Human-Robot Interaction. In Workshop on Exploring Applications for Autonomous Non-Verbal Human-Robot Interactions (HRI). March. Virtual: ACM. Available at: https://sites.google.com/view/non-verbal-hri-2021/home.

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