Publications

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[ Author(Desc)] Title Type Year
A
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.
Akgun, S.Alperen et al., 2020. Using Emotions to Complement Multi-Modal Human-Robot Interaction in Urban Search and Rescue Scenarios. In 22nd International Conference on Multimodal Interaction (ICMI-2020). October. Utrecht, the Netherlands, p. 9.
Allada, A.Krishna et al., 2021. Analysis of Language Embeddings for Classification of Unstructured Pathology Reports. In International Conference of the IEEE Engineering in Medicine and Biology Society. November. IEEE, p. 4.
B
Bellinger, C. et al., 2021. Active Measure Reinforcement Learning for Observation Cost Minimization: A framework for minimizing measurement costs in reinforcement learning. In Canadian Conference on Artificial Intelligence. Springer, p. 12.
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
Bhalla, S. et al., 2019. Compact Representation of a Multi-dimensional Combustion Manifold Using Deep Neural Networks. In European Conference on Machine Learning. Wurzburg, Germany, p. 8.
ecml_combustion_ml.pdf
Bhalla, S., Subramanian, S.G. & Crowley, M., 2019. Training Cooperative Agents for Multi-Agent Reinforcement Learning. In Proc. of the 18th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2019). Montreal, Canada.
C
Carrillo, J. et al., 2019. Comparison of Deep Learning models for Determining Road Surface Condition from Roadside Camera Images and Weather Data. In The Transportation Association of Canada and Intelligent Transportation Systems Canada Joint Conference (TAC-ITS). Halifax, Canada, p. 16.
Carrillo, J. & Crowley, M., 2019. Integration of Roadside Camera Images and Weather Data for monitoring Winter Road Surface Conditions. In Canadian Association of Road Safety Professionals CARSP Conference. CARSP Conference, Calgary, Alberta. , p. 4 (Won best paper award!). Available at: http://www.carsp.ca/research/research-papers/research-papers-search/download-info/integration-of-roadside-camera-images-and-weather-data-for-monitoring-winter-road-surface-conditions/.
mltree.pdf papers2.pdf papers1.pdf Final Published Version
Crowley, M., 2021. Prediction and Causality: How Can Machine Learning be Used for COVID-19?. In "What Needs to be done in order to Curb the Spread of Covid-19: Exposure Notification, Legal Considerations, and Statistical Modeling", a Conference on Data and Privacy during a Global Pandemic. July. Waterloo, Canada: Master of Public Service (MPS) Policy and Data Lab, University of Waterloo, p. 6. Available at: https://uwaterloo.ca/master-of-public-service/events/data-and-privacy-during-global-pandemic-conference.
Crowley, M., 2015. Answering Simple Questions About Spatially Spreading Systems. In 2015 Summer Solstice: 7th International Conference on Discrete Models of Complex Systems.
Crowley, M., 2013. Policy Gradient Optimization Using Equilibrium Policies for Spatial Planning Domains. In 13th INFORMS Computing Society Conference. Santa Fe, NM, United States.
Crowley, M., 2011. Equilibrium Policy Gradients for Spatiotemporal Planning. University of British Columbia. Available at: http://hdl.handle.net/2429/38971.
Crowley, M. & Poole, D., 2011. Policy gradient planning for environmental decision making with existing simulators. In 25th AAAI Conference on Artificial Intelligence (AAAI-11). San Francisco, pp. 1323–1330. Available at: https://www.scopus.com/record/display.uri?eid=2-s2.0-80055051332&origin=inward&txGid=de2006c39235aac9ba20cf0e76073dd9.
Crowley, M., Nelson, J. & Poole, D., 2009. Seeing the Forest Despite the Trees : Large Scale Spatial-Temporal Decision Making. In Conference on Uncertainty in Artificial Intelligence (UAI09). Montreal, Canada, pp. 126–134. Available at: http://www.cs.ubc.ca/ crowley/papers/uai09-mark-crowley.pdf.
Crowley, M., Nelson, J. & Poole, D., 2009. Seeing the Forest Despite the Trees : Large Scale Spatial-Temporal Decision Making. In Conference on Uncertainty in Artificial Intelligence (UAI09). Montreal, Canada, pp. 126–134. Available at: http://www.cs.ubc.ca/ crowley/papers/uai09-mark-crowley.pdf.

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