Kothig, A., Ilievski, M., Grasse, L., Rea, F., & Tata, M. (2019). A Bayesian System for Noise-Robust Binaural Sound Localisation for Humanoid Robots Presented at the A Bayesian System for Noise-Robust Binaural Sound Localisation for Humanoid Robots conference. IEEE. https://doi.org/10.1109/ROSE.2019.8790411 (Original work published 2019)
References
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Ernst, G., Sedwards, S., Zhang, Z., & Hasuo, I. (2019). Fast Falsification of Hybrid Systems using Probabilistically Adaptive Input Glasgow, Scotland: Springer.
Phan, B. T., Khan, S., Salay, R., & Czarnecki, K. (2019). Bayesian Uncertainty Quantification with Synthetic Data Presented at the Bayesian Uncertainty Quantification With Synthetic Data conference. Turku, Finland: SAFECOMP. Retrieved from https://www.waise.org/ (Original work published 2019)
Hurl, B. (2019). Local and Cooperative Autonomous Vehicle Perception from Synthetic Datasets Waterloo. Retrieved from https://uwspace.uwaterloo.ca/handle/10012/15118 (Original work published 2019)
Balasubramanian, V. (2019). 3D Online Multi-Object Tracking for Autonomous Driving Waterloo. Retrieved from https://uwspace.uwaterloo.ca/handle/10012/14994 (Original work published 2019)
Li, C., & Czarnecki, K. (2019). Rethinking Expected Cumulative Reward Formalism of Reinforcement Learning: A Micro-Objective Perspective Presented at the Rethinking Expected Cumulative Reward Formalism of Reinforcement Learning: A Micro-Objective Perspective conference. Montreal.
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