Prof. Marcus Brubaker

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Academic Advisor

PhD Computer Science, University of Toronto

Marcus Brubaker is Research Director of Borealis AI’s Toronto research center. He is also Assistant Professor of Computer Science at York University and Adjunct Professor in the University of Toronto Department of Computer Science. Originally from the United States, he moved to Canada in 2001 to study at the University of Toronto where he received his PhD in 2011. He also did postdocs at the Toyota Technological Institute at Chicago, Toronto Rehabilitation Hospital and the University of Toronto. His research interests span computer vision, machine learning and statistics.

Dr. Brubaker is a member of the Centre for Vision Research and core member of the Vision: Science to Application (VISTA) program at York University. He is also currently serving as an Associate Editor for the journal IET Computer Vision, an Area Chair for ECCV 2018 and Student Volunteer Chair for CVPR 2018.
 

Research Areas

Computer Vision

Publications

Oct. 26, 2020 Wavelet Flow: Fast Training of High Resolution Normalizing Flows
Conference on Neural Information Processing Systems (NeurIPS), 2020
Authors: J. Yu, K. Derpanis, M. Brubaker
Oct. 26, 2020 Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows
Neural Information Processing Systems (NeurIPS), 2020
Authors: R. Deng, B. Chang, M. Brubaker , G. Mori , A. Lehrmann
July 13, 2020 Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows
Workshop on Invertible Neural Networks, Normalizing Flows and Explicit Likelihood Models(ICML), 2020
Authors: R. Deng, B. Chang, M. Brubaker , G. Mori , A. Lehrmann
July 12, 2020 Tails of Lipschitz Triangular Flows
International Conference on Machine Learning (ICML), 2020
Authors: P. Jaini, I. Kobyzev , Y. Yu, M. Brubaker
June 6, 2020 Normalizing Flows: An Introduction and Review of Current Methods
IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE), 2020
Authors: I. Kobyzev , S. Prince , M. Brubaker
Feb. 7, 2020 Diachronic Embedding for Temporal Knowledge Graph Completion
Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020
Authors: R. Goel, S. M. Kazemi , M. Brubaker , P. Poupart
Dec. 14, 2019 Point Process Flows
Workshop on Learning with Temporal Point Processes (NeurIPS), 2019
Authors: *N. Mehrasa, *R. Deng, M. O. Ahmed , B. Chang, J. He , T. Durand , M. Brubaker , G. Mori
* Denotes equal contribution
Oct. 27, 2019 Noise Flow: Noise Modeling with Conditional Normalizing Flows
International Conference on Computer Vision (ICCV), 2019
Authors: A. Abdelhamed, M. Brubaker , M. S. Brown
Aug. 10, 2019 On the Effectiveness of Low Frequency Perturbations
International Joint Conference on Artificial Intelligence (IJCAI), 2019
Authors: Y. Sharma, G. W. Ding , M. Brubaker
Dec. 3, 2018 On Learning Wire-Length Efficient Neural Networks
Workshop on Compact Deep Neural Network Representation (NeurIPS), 2018
Authors: L. Wang, G. Castiglione , C. Srinivasa , M. Brubaker
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