Publications

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[ Author(Desc)] Title Type Year
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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.
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Salem, M., Crowley, M. & Fischmeister, S., 2016. Anomaly Detection Using Inter-Arrival Curves for Real-time Systems. In 2016 28th Euromicro Conference on Real-Time Systems. jul. Toulouse, France, pp. 97–106.
Salem, M., Crowley, M. & Fischmeister, S., 2016. Inter-Arrival Curves for Multi-Mode and Online Anomaly Detection. In Euromicro Conference on Real-Time Systems 2016 - Work-in-Progress Proceedings. Toulouse, France.
Salem, M., Crowley, M. & Fischmeister, S., 2016. Inter-Arrival Curves for Multi-Mode and Online Anomaly Detection. In Euromicro Conference on Real-Time Systems 2016 - Work-in-Progress Proceedings. Toulouse, France.
Sikaroudi, M. et al., 2021. Batch-Incremental Triplet Sampling for Training Triplet Networks Using Bayesian Updating Theorem. In 25th International Conference on Pattern Recognition (ICPR). January. Milan, Italy (virtual): IEEE, p. 7. Available at: https://ieeexplore.ieee.org/document/9412478.
Sikaroudi, M. et al., 2021. Magnification Generalization for Histopathology Image Embedding. In IEEE International Symposium on Biomedical Imaging (ISBI). April. p. 5.
Sikaroudi, M. et al., 2020. Offline versus Online Triplet Mining based on Extreme Distances of Histopathology Patches. In International Conference on Intelligent Systems and Computer Vision (ISCV 2020) . Fez-Morrocco (virtual): IEEE, p. 8. Available at: https://arxiv.org/abs/2007.02200.
2007.02200.pdf
Sikaroudi, M. et al., 2020. Supervision and Source Domain Impact on Representation Learning: A Histopathology Case Study. In International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'20). 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'20): IEEE Engineering in Medicine and Biology Society. Available at: https://embs.papercept.net/conferences/scripts/rtf/EMBC20_ContentListWeb_1.html#moat2-15_02.
2005.08629.pdf
Subramanian, S.Ganapathi et al., 2021. Partially Observable Mean Field Reinforcement Learning. In Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS). 3–7 May. London, United Kingdom: International Foundation for Autonomous Agents and Multiagent Systems, pp. 537-545.
Subramanian, S.Ganapthi, Bhalla, S. & Crowley, M., 2019. Learning Multi-Agent Communication with Reinforcement Learning. In Conference on Reinforcement Learning and Decision Making (RLDM-19). Montreal, Canada., p. 4.
Subramanian, S.Ganapathi & Crowley, M., 2018. Combining MCTS and A3C for prediction of spatially spreading processes in forest wildfire settings. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Toronto, Ontario, Canada, pp. 285–291.
Subramanian, S.G. & Crowley, M., 2018. Combining MCTS and A3C for Prediction of Spatially Spreading Processes in Forest Wildfire Settings. In Canadian Conference on Artificial Intelligence. Toronto, Ontario, Canada: Springer, pp. 285-291. Available at: https://link.springer.com/chapter/10.1007/978-3-319-89656-4_28.
Canadian_AI_forestfire_2.pdf
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
Subramanian, S.Ganapathi & Crowley, M., 2017. Learning Forest Wildfire Dynamics from Satellite Images Using Reinforcement Learning. In Conference on Reinforcement Learning and Decision Making. Ann Arbor, MI, USA.
Subramanian_Crowley_-_2017_-_Learning_Forest_Wildfire_Dynamics_from_Satellite_Images_Using_Reinforcement_Learning.pdf
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Taleghan, M.A. et al., 2015. PAC Optimal MDP Planning with Application to Invasive Species Management. Journal of Machine Learning Research, 16, pp.3877–3903. Available at: http://jmlr.org/papers/v16/taleghan15a.html.

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