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Renjie Liao

Renjie is a PhD student in the Machine Learning Group of the Department of Computer Science, University of Toronto, supervised by Professor Raquel Urtasun and Professor Richard Zemel. He is also a Senior Research Scientist in Uber’s Advanced Technologies Group R&D and affiliated with the Vector Institute. Renjie received his MPhil from the Department of Computer Science and Engineering at the Chinese University of Hong Kong, under the supervision of Professor Jiaya Jia. He got his BEng from the School of Automation Science and Electrical Engineering at Beihang University (formerly Beijing University of Aeronautics and Astronautics).

Recent publications

Learning Lane Graph Representations for Motion Forecasting

Ming Liang, Bin Yang, Rui Hu, Yun Chen, Renjie Liao, Song Feng, Raquel Urtasun (ECCV 2020, oral)

Testing the Safety of Self-driving Vehicles by Simulating Perception and Prediction

Kelvin Wong, Qiang Zhang, Ming Liang, Bin Yang, Renjie Liao, Abbas Sadat, Raquel Urtasun (ECCV 2020)

DSDNet: Deep Structured self-Driving Network

Wenyuan Zeng, Shenlong Wang, Renjie Liao, Yun Chen, Bin Yang, Raquel Urtasun (ECCV 2020)

Implicit Latent Variable Model for Scene-Consistent Motion Forecasting

Sergio Casas, Cole Gulino, Simon Suo, Katie Luo, Renjie Liao, Raquel Urtasun (ECCV 2020)

SpAGNN: Spatially-Aware Graph Neural Networks for Relational Behavior Forecasting from Sensor Data

Sergio Casas, Cole Gulino, Renjie Liao, Raquel Urtasun
(ICRA 2020, oral)

Incremental Few-Shot Learning with Attention Attractor Networks

Mengye Ren, Renjie Liao, Ethan Fetaya, Richard S. Zemel (NeurIPS 2019)

Efficient Graph Generation with Graph Recurrent Attention Networks

Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, William L. Hamilton, David Duvenaud, Raquel Urtasun, Richard S. Zemel (NeurIPS 2019)

Discrete Residual Flow for Probabilistic Pedestrian Behavior Prediction

Ajay Jain* , Sergio Casas*, Renjie Liao*, Yuwen Xiong*, Song Feng, Sean Segal, Raquel Urtasun (CoRL 2019)

DMM-Net: Differentiable Mask-Matching Network for Video Object Segmentation

Xiaohui Zeng, Renjie Liao, Li Gu, Yuwen Xiong, Sanja Fidler, Raquel Urtasun (ICCV 2019)

UPSNet: A Unified Panoptic Segmentation Network

Yuwen Xiong*, Renjie Liao*, Hengshuang Zhao*, Rui Hu, Min Bai, Ersin Yumer, Raquel Urtasun (CVPR 2019, oral)

DARNet: Deep Active Ray Network for Building Segmentation

Dominic Cheng, Renjie Liao, Sanja Fidler, Raquel Urtasun (CVPR 2019)

Lorentzian Distance Learning for Hyperbolic Representations

Marc T. Law, Renjie Liao, Jake Snell, Richard S. Zemel (ICML 2019, oral)

LanczosNet: Multi-Scale Deep Graph Convolutional Networks

Renjie Liao, Zhizhen Zhao, Raquel Urtasun, Richard S. Zemel (ICLR 2019)

Neural Guided Constraint Logic Programming for Program Synthesis

Lisa Zhang, Gregory Rosenblatt, Ethan Fetaya, Renjie Liao, William E. Byrd, Matthew Might, Raquel Urtasun, Richard Zemel (NeurIPS 2018)

Incremental Few-Shot Learning with Attention Attractor Networks

Mengye Ren, Renjie Liao, Ethan Fetaya, Richard S. Zemel (NeurIPS 2018, Meta Learning workshop, spotlight)

GeoNet: Geometric Neural Network for Joint Depth and Surface Normal Estimation

Xiaojuan Qi, Renjie Liao, Zhengzhe Liu, Raquel Urtasun, Jiaya Jia (CVPR 2018)

Learning Deep Structured Active Contours End-to-End

Diego Marcos, Devis Tuia, Benjamin Kellenberger, Lisa Zhang, Min Bai, Renjie Liao, Raquel Urtasun (CVPR 2018)

Reviving and Improving Recurrent Back-Propagation

Renjie Liao*, Yuwen Xiong*, Ethan Fetaya, Lisa Zhang, KiJung Yoon, Xaq Pitkow, Raquel Urtasun, Richard Zemel (ICML 2018, oral)

Inference in Probabilistic Graphical Models by Graph Neural Networks

KiJung Yoon, Renjie Liao, Yuwen Xiong, Lisa Zhang, Ethan Fetaya, Raquel Urtasun, Richard Zemel, Xaq Pitkow (ICLR 2018, workshop)

Leveraging Constraint Logic Programming for Neural Guided Program Synthesis

Lisa Zhang, Gregory Rosenblatt, Ethan Fetaya, Renjie Liao, William E. Byrd, Raquel Urtasun, Richard Zemel (ICLR 2018)

Graph Partition Neural Networks for Semi-Supervised Classification

Renjie Liao*, Marc Brockschmidt, Daniel Tarlow*, Alexander L. Gaunt, Raquel Urtasun, Richard Zemel (ICLR 2018)

NerveNet: Learning Structured Policy with Graph Neural Networks

Tingwu Wang*, Renjie Liao*, Jimmy Ba, Sanja Fidler (ICLR 2018)

Understanding Short-Horizon Bias in Stochastic Meta-Optimization

Yuhuai Wu*, Mengye Ren*, Renjie Liao, Roger B. Grosse (ICLR 2018)

Situation Recognition with Graph Neural Networks

Ruiyu Li, Makarand Tapaswi, Renjie Liao, Jiaya Jia, Raquel Urtasun, Sanja Fidler (ICCV 2017)

3D Graph Neural Networks for RGBD Semantic Segmentation

Xiaojuan Qi, Renjie Liao, Jiaya Jia, Sanja Fidler, Raquel Urtasun (ICCV 2017, oral)

Detail-Revealing Deep Video Super-Resolution

Xin Tao, Hongyun Gao, Renjie Liao, Jue Wang, Jiaya Jia (ICCV 2017, oral)

Normalizing the Normalizers: Comparing and Extending Network Normalization Schemes

Mengye Ren*, Renjie Liao*, Raquel Urtasun, Fabian H. Sinz, Richard S. Zemel (ICLR 2017)