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
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]
Conference on Computer Vision and Pattern ( 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]
Conference on Computer Vision and Pattern ( ICML), 2018
