Selected Publications (Full List)

* below indicates equal contribution

	LookOut: Diverse Multi-Future Prediction and Planning for Self-Driving Alexander Cui, Abbas Sadat, Sergio Casas, Renjie Liao, Raquel Urtasun. International Conference on Computer Vision (ICCV), 2021 [Oral Presentation, 210/6236 (3%)]
	Imitation Learning From Inconcurrent Multi-Agent Interactions Xin Zhang, Weixiao Huang, Yanhua Li, Renjie Liao, Ziming Zhang. The 60th IEEE Conference on Decision and Control (CDC), 2021
	LaneRCNN: Distributed Representations for Graph-Centric Motion Forecasting Wenyuan Zeng, Ming Liang, Renjie Liao, Raquel Urtasun. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021
	Safety-Oriented Pedestrian Motion and Scene Occupancy Forecasting Katie Luo, Sergio Casas, Renjie Liao, Xinchen Yan, Yuwen Xiong, Wenyuan Zeng, Raquel Urtasun. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021
	NP-DRAW: A Non-parametric Structured Latent Variable Model for Image Generation Xiaohui Zeng, Raquel Urtasun, Richard S. Zemel, Sanja Fidler, Renjie Liao The Conference on Uncertainty in Artificial Intelligence (UAI), 2021 [Code]
	Nonlinear Equation Solving: A Faster Alternative to Feedforward Computation Yang Song, Chenlin Meng, Renjie Liao, Stefano Ermon International Conference on Machine Learning (ICML), 2021 [Code]
	Structure-Coherent Deep Feature Learning for Robust Face Alignment Chunze Lin, Beier Zhu, Quan Wang, Renjie Liao, Chen Qian, Jiwen Lu, Jie Zhou. IEEE Transactions on Image Processing (TIP), 2021 [arXiv Version]
	Deep Learning on Graphs: Theory, Models, Algorithms and Applications Renjie Liao PhD Thesis, 2021
	A PAC-Bayesian Approach to Generalization Bounds for Graph Neural Networks Renjie Liao, Raquel Urtasun, Richard S. Zemel International Conference on Learning Representations (ICLR), 2021
	Latent Variable Modelling with Hyperbolic Normalizing Flows Joey Bose, Ariella Smofsky, Renjie Liao, Prakash Panangaden, William L. Hamilton International Conference on Machine Learning (ICML), 2020 [Code]
	GeoNet++: Iterative Geometric Neural Network with Edge-Aware Refinement for Joint Depth and Surface Normal Estimation Xiaojuan Qi, Zhengzhe Liu, Renjie Liao, Philip H.S. Torr, Raquel Urtasun, Jiaya Jia IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2020 [Code]
	Learning Lane Graph Representations for Motion Forecasting Ming Liang, Bin Yang, Rui Hu, Yun Chen, Renjie Liao, Song Feng, Raquel Urtasun European Conference on Computer Vision (ECCV), 2020 [Code][ Oral Presentation, 104/5025 (2%)]
	DSDNet: Deep Structured Self-Driving Network Wenyuan Zeng, Shenlong Wang, Renjie Liao, Yun Chen, Bin Yang, Raquel Urtasun European Conference on Computer Vision (ECCV), 2020
	Implicit Latent Variable Model for Scene-Consistent Motion Forecasting Sergio Casas, Cole Gulino, Simon Suo, Katie Luo, Renjie Liao, Raquel Urtasun European Conference on Computer Vision (ECCV), 2020
	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 European Conference on Computer Vision (ECCV), 2020
	Spatially-Aware Graph Neural Networks for Relational Behavior Forecasting from Sensor Data Sergio Casas, Cole Gulino, Renjie Liao, Raquel Urtasun International Conference on Robotics and Automation (ICRA), 2020
	Efficient Graph Generation with Graph Recurrent Attention Networks Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, Charlie Nash, William L. Hamilton, David Duvenaud, Raquel Urtasun, Richard S. Zemel Neural Information Processing Systems (NeurIPS), 2019 [Code]
	Incremental Few-Shot Learning with Attention Attractor Networks Mengye Ren, Renjie Liao, Ethan Fetaya, Richard S. Zemel Neural Information Processing Systems (NeurIPS), 2019 [Code]
	DMM-Net: Differentiable Mask-Matching Network for Video Object Segmentation Xiaohui Zeng, Renjie Liao*, Li Gu, Yuwen Xiong, Sanja Fidler, Raquel Urtasun International Conference on Computer Vision* (ICCV), 2019 [Code] [Video]
	Discrete Residual Flow for Probabilistic Pedestrian Behavior Prediction Ajay Jain, Sergio Casas Romero, Renjie Liao, Yuwen Xiong, Song Feng, Sean Segal, Raquel Urtasun Conference on Robot Learning (CoRL), 2019
	Lorentzian Distance Learning for Hyperbolic Representations Marc T. Law, Renjie Liao, Jake Snell, Richard S. Zemel International Conference on Machine Learning (ICML), 2019 [Code]
	Inference in Probabilistic Graphical Models by Graph Neural Networks KiJung Yoon, Renjie Liao, Yuwen Xiong, Lisa Zhang, Ethan Fetaya, Raquel Urtasun, Richard S. Zemel, Xaq Pitkow ICML Workshop on Tractable Probabilistic Modeling, 2019 [ICLR 2018 Workshop][Code Coming Soon!] [Best Paper Award]
	UPSNet: A Unified Panoptic Segmentation Network Yuwen Xiong, Renjie Liao*, Hengshuang Zhao, Rui Hu, Min Bai, Ersin Yumer, Raquel Urtasun International Conference on Computer Vision and Pattern Recognition (CVPR), 2019 [Code] [ Oral Presentation, 288/5160 (5.6%)]
	DARNet: Deep Active Ray Network for Building Segmentation Dominic Cheng, Renjie Liao, Sanja Fidler, Raquel Urtasun International Conference on Computer Vision and Pattern Recognition (CVPR), 2019 [Code]
	LanczosNet: Multi-Scale Deep Graph Convolutional Networks Renjie Liao, Zhizhen Zhao, Raquel Urtasun, Richard S. Zemel International Conference on Learning Representations (ICLR), 2019 [Code] [NeurIPS 2018 R2L Workshop] [ Score Rank: 69/1591 (4.4%) ]
	Neural Guided Constraint Logic Programming for Program Synthesis Lisa Zhang, Gregory Rosenblatt, Ethan Fetaya, Renjie Liao, William E. Byrd, Matthew Might, Raquel Urtasun, Richard S. Zemel Neural Information Processing Systems (NeurIPS), 2018 [Code] [ICLR 2018 Workshop]
	Reviving and Improving Recurrent Back-Propagation Renjie Liao, Yuwen Xiong, Ethan Fetaya, Lisa Zhang, KiJung Yoon, Xaq Pitkow, Raquel Urtasun, Richard S. Zemel International Conference on Machine Learning (ICML), 2018 [Code] [Video] [ Full Oral Presentation, 212/2473 (8.6%) ]
	Graph Partition Neural Networks for Semi-Supervised Classification Renjie Liao, Marc Brockschmidt, Daniel Tarlow, Alexander Gaunt, Raquel Urtasun, Richard S. Zemel International Conference on Learning Representations Workshop (ICLR), 2018 [Code]
	NerveNet: Learning Structured Policy with Graph Neural Networks Tingwu Wang, Renjie Liao, Jimmy Ba, Sanja Fidler International Conference on Learning Representations (ICLR), 2018 [Project] [Code] [Video]
	Understanding Short-Horizon Bias in Stochastic Meta-Optimization Yuhuai Wu, Mengye Ren, Renjie Liao, Roger Grosse International Conference on Learning Representations (ICLR), 2018 [Code]
	GeoNet: Geometric Neural Network for Joint Depth and Surface Normal Estimation Xiaojuan Qi, Renjie Liao, Zhengzhe Liu, Raquel Urtasun, Jiaya Jia International Conference on Computer Vision and Pattern Recognition (CVPR), 2018 [Code]
	Learning Deep Structured Active Contours End-to-End Diego Marcos, Devis Tuia, Benjamin Kellenberger, Lisa Zhang, Min Bai, Renjie Liao, Raquel Urtasun International Conference on Computer Vision and Pattern Recognition (CVPR), 2018 [Code] [Spotlight Presentation, 224/3303 (6.8%)]
	3D Graph Neural Networks for RGBD Semantic Segmentation Xiaojuan Qi, Renjie Liao, Jiaya Jia, Sanja Fidler, Raquel Urtasun IEEE International Conference on Computer Vision (ICCV), 2017 [Code] [ PyTorch Implementation ] [Oral Presentation, 45/2143 (2.1%)]
	Situation Recognition with Graph Neural Networks Ruiyu Li, Makarand Tapaswi, Renjie Liao, Jiaya Jia, Raquel Urtasun, Sanja Fidler IEEE International Conference on Computer Vision (ICCV), 2017 [Code]
	Detail-revealing Deep Video Super-Resolution Xin Tao, Hongyun Gao, Renjie Liao, Jue Wang, Jiaya Jia IEEE International Conference on Computer Vision (ICCV), 2017 [Code] [Oral Presentation, 45/2143 (2.1%)]
	Normalizing the Normalizers: Comparing and Extending Network Normalization Schemes Mengye Ren, Renjie Liao, Raquel Urtasun, Fabian H. Sinz, Richard S. Zemel International Conference on Learning Representations (ICLR), 2017 [Code]
	Learning to Generate Images with Perceptual Similarity Metrics Jake Snell, Karl Ridgeway, Renjie Liao, Brett D. Roads, Michael C. Mozer, Richard S. Zemel International Conference on Image Processing (ICIP), 2017
	Learning Deep Parsimonious Representation Renjie Liao, Alexander Schwing, Richard S. Zemel, Raquel Urtasun Neural Information Processing Systems (NIPS), 2016 [Code]
	Video Super-Resolution via Deep Draft-Ensemble Learning Renjie Liao, Xin Tao, Ruiyu Li, Ziyang Ma, Jiaya Jia IEEE International Conference on Computer Vision (ICCV), 2015 [Project & Code]
	Semantic Segmentation With Object Clique Potential Xiaojuan Qi, Jianping Shi, Shu Liu, Renjie Liao, Jiaya Jia IEEE International Conference on Computer Vision (ICCV), 2015
	Handling Motion Blur in Multi-Frame Super-Resolution Ziyang Ma, Renjie Liao, Xin Tao, Li Xu, Jiaya Jia, Enhua Wu International Conference on Computer Vision and Pattern Recognition (CVPR), 2015 [Project & Code]
	Deep Edge-Aware Filters Li Xu, Jimmy Ren, Qiong Yan, Renjie Liao, Jiaya Jia International Conference on Machine Learning (ICML), 2015 [Code]
	Nonparametric Bayesian Upstream Supervised Multi-Modal Topic Models Renjie Liao, Jun Zhu, Zengchang Qin ACM International Conference on Web Search and Data Mining (WSDM), 2014
	Learning Important Spatial Pooling Regions for Scene Classification Di Lin, Cewu Lu, Renjie Liao, Jiaya Jia International Conference on Computer Vision and Pattern Recognition (CVPR), 2014
	CoDeL: An Efficient Human Co-detection and Labeling Framework Jianping Shi, Renjie Liao, Jiaya Jia IEEE International Conference on Computer Vision (ICCV), 2013 [Project]
	Image Super-Resolution Using Local Learnable Kernel Regression Renjie Liao, Zengchang Qin Asian Conference on Computer Vision (ACCV), 2012 [Code]

Preprints

	Deformable Filter Convolution for Point Cloud Reasoning Yuwen Xiong, Mengye Ren, Renjie Liao, Kelvin Wong, Raquel Urtasun arXiv preprint arXiv:1907.13079 (2019)
	Alchemy: A Quantum Chemistry Dataset for Benchmarking AI Models Guangyong Chen, Pengfei Chen, Chang-Yu Hsieh, Chee-Kong Lee, Benben Liao, Renjie Liao, Weiwen Liu, Jiezhong Qiu, Qiming Sun, Jie Tang, Richard S. Zemel, Shengyu Zhang arXiv preprint arXiv:1906.09427 (2019) [Dataset + Competition]
	Bounded-Distortion Metric Learning Renjie Liao, Jianping Shi, Ziyang Ma, Jun Zhu, Jiaya Jia. arXiv preprint arXiv:1505.02377 (2015) [Code]