Wenyuan Zeng
Wenyuan Zeng is currently a PhD student at the University of Toronto, supervised by Prof. Raquel Urtasun. His research interest mainly lies in deep learning, computer vision and decision making process. At the same time, he is also working full-time at Uber ATG Toronto to apply his research work to the development of self-driving cars, focusing on perception, prediction and planning. Before coming to University of Toronto, Wenyuan Zeng finished his Bachelor degree in Tsinghua University, China, majored in Mathematics and Physics.
Research Papers
End-to-end Interpretable Neural Motion Planner
W. Zeng, W. Luo, S. Suo, A. Sadat, B. Yang, S. Casas, R. Urtasun
In this paper, we propose a neural motion planner for learning to drive autonomously in complex urban scenarios that include traffic-light handling, yielding, and interactions with multiple road-users. Towards this goal, we design a holistic model that takes as input raw LIDAR data and an HD map and produces interpretable intermediate representations in the form of 3D detections and their future trajectories, as well as a cost volume defining the goodness of each position that the self-driving car can take within the planning horizon. [...] [PDF]
Conference on Computer Vision and Pattern Recognition (CVPR), 2019
In this paper, we propose a neural motion planner for learning to drive autonomously in complex urban scenarios that include traffic-light handling, yielding, and interactions with multiple road-users. Towards this goal, we design a holistic model that takes as input raw LIDAR data and an HD map and produces interpretable intermediate representations in the form of 3D detections and their future trajectories, as well as a cost volume defining the goodness of each position that the self-driving car can take within the planning horizon. [...] [PDF]
Conference on Computer Vision and Pattern Recognition (CVPR), 2019
Differentiable Compositional Kernel Learning for Gaussian Processes
S. Sun, G. Zhang, C. Wang, W. Zeng, J. Li, R. Grosse
The generalization properties of Gaussian processes depend heavily on the choice of kernel, and this choice remains a dark art. We present the Neural Kernel Network (NKN), a flexible family of kernels represented by a neural network. [...] [PDF]
International Conference on Machine Learning (ICML), 2018
The generalization properties of Gaussian processes depend heavily on the choice of kernel, and this choice remains a dark art. We present the Neural Kernel Network (NKN), a flexible family of kernels represented by a neural network. [...] [PDF]
International Conference on Machine Learning (ICML), 2018
Learning to Reweight Examples for Robust Deep Learning
M. Ren, W. Zeng, B. Yang, R. Urtasun
Deep neural networks have been shown to be very powerful modeling tools for many supervised learning tasks involving complex input patterns. However, they can also easily overfit to training set biases and label noises. [...] [PDF]
International Conference on Machine Learning ( ICML), 2018
Deep neural networks have been shown to be very powerful modeling tools for many supervised learning tasks involving complex input patterns. However, they can also easily overfit to training set biases and label noises. [...] [PDF]
International Conference on Machine Learning ( ICML), 2018