Skip to footer

Yuwen Xiong

Avatar
0 BLOG ARTICLES 4 RESEARCH PAPERS
Yuwen Xiong is a graduate student in Machine Learning Group at the University of Toronto, and a Research Scientist at Uber ATG Toronto, both supervised by Prof. Raquel Urtasun. Before that he received his bachelor degree in Computer Science from Zhejiang University in June 2018. His research interests include Computer Vision and Machine Learning, especially Deep Learning.

Research Papers

Deep Rigid Instance Scene Flow

W.-C. Ma, S. Wang, R. Hu, Y. Xiong, R. Urtasun
In this paper we tackle the problem of scene flow estimation in the context of self-driving. We leverage deep learning techniques as well as strong priors as in our application domain the motion of the scene can be composed by the motion of the robot and the 3D motion of the actors in the scene. [...] [PDF]
Conference on Computer Vision and Pattern Recognition (CVPR), 2019

UPSNet: A Unified Panoptic Segmentation Network

Y. Xiong, R. Liao, H. Zhao, R. Hu, M. Bai, E. Yumer, R. Urtasun
In this paper we tackle the problem of scene flow estimation in the context of self-driving. We leverage deep learning techniques as well as strong priors as in our application domain the motion of the scene can be composed by the motion of the robot and the 3D motion of the actors in the scene. [...] [PDF]
Conference on Computer Vision and Pattern Recognition (CVPR), 2019

Reviving and Improving Recurrent Back Propagation

R. Liao, Y. Xiong, E. Fetaya, L. Zhang, K. Yoon, X. Pitkow, R. Urtasun, R. Zemel
In this paper, we revisit the recurrent back-propagation (RBP) algorithm, discuss the conditions under which it applies as well as how to satisfy them in deep neural networks. We show that RBP can be unstable and propose two variants based on conjugate gradient on the normal equations (CG-RBP) and Neumann series (Neumann-RBP). [...] [PDF]
Conference on Computer Vision and Pattern Recognition (ICML), 2018

Inference in Probabilistic Graphical Models by Graph Neural Networks

K. Yoon, R. Liao, Y. Xiong, L. Zhang, E. Fetaya, R. Urtasun, R. Zemel, X. Pitkow
A fundamental computation for statistical inference and accurate decision-making is to compute the marginal probabilities or most probable states of task-relevant variables. Probabilistic graphical models can efficiently represent the structure of such complex data, but performing these inferences is generally difficult. [...] [PDF]
Workshop @ International Conference on Learning Representations (ICLR), 2018

Popular Articles