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Wenyuan Zeng

Wenyuan is currently a PhD student at the University of Toronto, supervised by Professor Raquel Urtasun. At the same time, he is working full-time at Uber ATG R&D to apply his research work to the development of self-driving vehicles, focusing on perception, prediction, and planning. His research interest mainly lies in deep learning, computer vision, and the decision-making process. Before arriving at the University of Toronto, Wenyuan finished his bachelor’s degree at Tsinghua University, China, majoring in mathematics and physics.

Recent publications

PnPNet: End-to-End Perception and Prediction with Tracking in the Loop

Ming Liang*, Bin Yang*, Wenyuan Zeng, Yun Chen, Rui Hu, Sergio Casas, Raquel Urtasun (CVPR 2020)

End-to-End Interpretable Neural Motion Planner

Wenyuan Zeng*, Wenjie Luo*, Simon Suo, Abbas Sadat, Bin Yang, Sergio Casas, Raquel Urtasun (CVPR 2019, oral)

Learning to Reweight Examples for Robust Deep Learning

Mengye Ren, Wenyuan Zeng, Bin Yang, Raquel Urtasun (ICML 2018, spotlight)

Differentiable Compositional Kernel Learning for Gaussian Processes

Shengyang Sun, Guodong Zhang, Chaoqi Wang, Wenyuan Zeng, Jiaman Li, Roger Grosse (ICML 2018)