A novel serial crime prediction model based on Bayesian learning theory. - Renjie Liao; Xueyao Wang; Lun Li; Zengchang Qin (ICMLC, 2010)

Improved Quantum Particle Swarm Optimization by Bloch Sphere. - Yu Du; Haibin Duan; Renjie Liao; Xihua Li 003 (ICSI (1), 2010)

Image Super-Resolution Using Local Learnable Kernel Regression. - Renjie Liao; Zengchang Qin (ACCV (3), 2012)

A robust fusion scheme for multifocus images using sparse features. - Tao Wan 001; Zengchang Qin; Chenchen Zhu; Renjie Liao (ICASSP, 2013)

CoDeL: A Human Co-detection and Labeling Framework. - Jianping Shi; Renjie Liao; Jiaya Jia (ICCV, 2013)

Learning Important Spatial Pooling Regions for Scene Classification. - Di Lin 002; Cewu Lu; Renjie Liao; Jiaya Jia (CVPR, 2014)

A confidence growing model for super-resolution. - Sina Lin; Zengchang Qin; Renjie Liao; Tao Wan 001 (ICIP, 2014)

Nonparametric bayesian upstream supervised multi-modal topic models. - Renjie Liao; Jun Zhu 001; Zengchang Qin (WSDM, 2014)

Handling motion blur in multi-frame super-resolution. - Ziyang Ma 002; Renjie Liao; Xin Tao 001; Li Xu 001; Jiaya Jia; Enhua Wu (CVPR, 2015)

Video Super-Resolution via Deep Draft-Ensemble Learning. - Renjie Liao; Xin Tao 001; Ruiyu Li; Ziyang Ma 002; Jiaya Jia (ICCV, 2015)

Semantic Segmentation with Object Clique Potential. - Xiaojuan Qi 001; Jianping Shi; Shu Liu 005; Renjie Liao; Jiaya Jia (ICCV, 2015)

Deep Edge-Aware Filters. - Li Xu 001; Jimmy S. J. Ren; Qiong Yan; Renjie Liao; Jiaya Jia (ICML, 2015)

Bounded-Distortion Metric Learning. - Renjie Liao; Jianping Shi; Ziyang Ma 002; Jun Zhu 001; Jiaya Jia (CoRR, 2015)

Personal object discovery in first-person videos. - Cewu Lu; Renjie Liao; Jiaya Jia (IEEE Trans. Image Process., 2015)

Learning Deep Parsimonious Representations. - Renjie Liao; Alexander G. Schwing; Richard S. Zemel; Raquel Urtasun (NIPS, 2016)

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 001; Renjie Liao; Jiaya Jia; Sanja Fidler; Raquel Urtasun (ICCV, 2017)

Detail-Revealing Deep Video Super-Resolution. - Xin Tao 001; Hongyun Gao 001; Renjie Liao; Jue Wang 001; Jiaya Jia (ICCV, 2017)

Learning to generate images with perceptual similarity metrics. - Jake Snell; Karl Ridgeway; Renjie Liao; Brett D. Roads; Michael C. Mozer; Richard S. Zemel (ICIP, 2017)

Normalizing the Normalizers: Comparing and Extending Network Normalization Schemes. - Mengye Ren; Renjie Liao; Raquel Urtasun; Fabian H. Sinz; Richard S. Zemel (ICLR (Poster), 2017)

Learning Deep Structured Active Contours End-to-End. - Diego Marcos; Devis Tuia; Benjamin Kellenberger; Lisa Zhang; Min Bai; Renjie Liao; Raquel Urtasun (CVPR, 2018)

GeoNet: Geometric Neural Network for Joint Depth and Surface Normal Estimation. - Xiaojuan Qi 001; Renjie Liao; Zhengzhe Liu; Raquel Urtasun; Jiaya Jia (CVPR, 2018)

Graph Partition Neural Networks for Semi-Supervised Classification. - Renjie Liao; Marc Brockschmidt; Daniel Tarlow; Alexander L. Gaunt; Raquel Urtasun; Richard S. Zemel (ICLR (Workshop), 2018)

NerveNet: Learning Structured Policy with Graph Neural Networks. - Tingwu Wang; Renjie Liao; Jimmy Ba; Sanja Fidler (ICLR (Poster), 2018)

Understanding Short-Horizon Bias in Stochastic Meta-Optimization. - Yuhuai Wu; Mengye Ren; Renjie Liao; Roger B. Grosse (ICLR (Poster), 2018)

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 (ICLR (Workshop), 2018)

Leveraging Constraint Logic Programming for Neural Guided Program Synthesis. - Lisa Zhang; Gregory Rosenblatt; Ethan Fetaya; Renjie Liao; William E. Byrd; Raquel Urtasun; Richard S. Zemel (ICLR (Workshop), 2018)

Reviving and Improving Recurrent Back-Propagation. - Renjie Liao; Yuwen Xiong; Ethan Fetaya; Lisa Zhang; KiJung Yoon; Xaq Pitkow; Raquel Urtasun; Richard S. Zemel (ICML, 2018)

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 (NeurIPS, 2018)

Discrete Residual Flow for Probabilistic Pedestrian Behavior Prediction. - Ajay Jain; Sergio Casas 002; Renjie Liao; Yuwen Xiong; Song Feng; Sean Segal; Raquel Urtasun (CoRL, 2019)

DARNet: Deep Active Ray Network for Building Segmentation. - Dominic Cheng; Renjie Liao; Sanja Fidler; Raquel Urtasun (CVPR, 2019)

UPSNet: A Unified Panoptic Segmentation Network. - Yuwen Xiong; Renjie Liao; Hengshuang Zhao; Rui Hu 001; Min Bai; Ersin Yumer; Raquel Urtasun (CVPR, 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)

LanczosNet: Multi-Scale Deep Graph Convolutional Networks. - Renjie Liao; Zhizhen Zhao 001; Raquel Urtasun; Richard S. Zemel (ICLR (Poster), 2019)

Lorentzian Distance Learning for Hyperbolic Representations. - Marc Teva Law; Renjie Liao; Jake Snell; Richard S. Zemel (ICML, 2019)

Efficient Graph Generation with Graph Recurrent Attention Networks. - Renjie Liao; Yujia Li 001; Yang Song 011; Shenlong Wang; William L. Hamilton; David Duvenaud; Raquel Urtasun; Richard S. Zemel (NeurIPS, 2019)

Incremental Few-Shot Learning with Attention Attractor Networks. - Mengye Ren; Renjie Liao; Ethan Fetaya; Richard S. Zemel (NeurIPS, 2019)

Alchemy: A Quantum Chemistry Dataset for Benchmarking AI Models. - Guangyong Chen; Pengfei Chen 003; Chang-Yu Hsieh; Chee-Kong Lee; Benben Liao; Renjie Liao; Weiwen Liu; Jiezhong Qiu; Qiming Sun; Jie Tang 001; Richard S. Zemel; Shengyu Zhang 002 (CoRR, 2019)

Deformable Filter Convolution for Point Cloud Reasoning. - Yuwen Xiong; Mengye Ren; Renjie Liao; Kelvin Wong; Raquel Urtasun (CoRR, 2019)

Efficient Graph Generation with Graph Recurrent Attention Networks. - Renjie Liao; Yujia Li 001; Yang Song 011; Shenlong Wang; Charlie Nash; William L. Hamilton; David Duvenaud; Raquel Urtasun; Richard S. Zemel (CoRR, 2019)

Spatially-Aware Graph Neural Networks for Relational Behavior Forecasting from Sensor Data. - Sergio Casas 002; Cole Gulino; Renjie Liao; Raquel Urtasun (CoRR, 2019)

Implicit Latent Variable Model for Scene-Consistent Motion Forecasting. - Sergio Casas 002; Cole Gulino; Simon Suo; Katie Luo; Renjie Liao; Raquel Urtasun (ECCV (23), 2020)

Learning Lane Graph Representations for Motion Forecasting. - Ming Liang; Bin Yang 021; Rui Hu 001; Yun Chen 014; Renjie Liao; Song Feng; Raquel Urtasun (ECCV (2), 2020)

Testing the Safety of Self-driving Vehicles by Simulating Perception and Prediction. - Kelvin Wong; Qiang Zhang; Ming Liang; Bin Yang 021; Renjie Liao; Abbas Sadat; Raquel Urtasun (ECCV (26), 2020)

DSDNet: Deep Structured Self-driving Network. - Wenyuan Zeng; Shenlong Wang; Renjie Liao; Yun Chen 014; Bin Yang 021; Raquel Urtasun (ECCV (21), 2020)

Latent Variable Modelling with Hyperbolic Normalizing Flows. - Avishek Joey Bose; Ariella Smofsky; Renjie Liao; Prakash Panangaden; William L. Hamilton (ICML, 2020)

SpAGNN: Spatially-Aware Graph Neural Networks for Relational Behavior Forecasting from Sensor Data. - Sergio Casas 002; Cole Gulino; Renjie Liao; Raquel Urtasun (ICRA, 2020)

Nonlinear Equation Solving: A Faster Alternative to Feedforward Computation. - Yang Song 011; Chenlin Meng; Renjie Liao; Stefano Ermon (CoRR, 2020)

Fast and Accurate: Structure Coherence Component for Face Alignment. - Beier Zhu; Chunze Lin; Quan Wang; Renjie Liao; Chen Qian 006 (CoRR, 2020)

Imitation Learning From Inconcurrent Multi-Agent Interactions. - Xin Zhang 098; Weixiao Huang; Yanhua Li; Renjie Liao; Ziming Zhang (CDC, 2021)

LookOut: Diverse Multi-Future Prediction and Planning for Self-Driving. - Alexander Cui; Sergio Casas 002; Abbas Sadat; Renjie Liao; Raquel Urtasun (ICCV, 2021)

A PAC-Bayesian Approach to Generalization Bounds for Graph Neural Networks. - Renjie Liao; Raquel Urtasun; Richard S. Zemel (ICLR, 2021)

Accelerating Feedforward Computation via Parallel Nonlinear Equation Solving. - Yang Song 011; Chenlin Meng; Renjie Liao; Stefano Ermon (ICML, 2021)

Safety-Oriented Pedestrian Occupancy Forecasting. - Katie Luo; Sergio Casas 002; Renjie Liao; Xinchen Yan; Yuwen Xiong; Wenyuan Zeng; Raquel Urtasun (IROS, 2021)

LaneRCNN: Distributed Representations for Graph-Centric Motion Forecasting. - Wenyuan Zeng; Ming Liang; Renjie Liao; Raquel Urtasun (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 (UAI, 2021)

Safety-Oriented Pedestrian Motion and Scene Occupancy Forecasting. - Katie Luo; Sergio Casas 002; Renjie Liao; Xinchen Yan; Yuwen Xiong; Wenyuan Zeng; Raquel Urtasun (CoRR, 2021)

NP-DRAW: A Non-Parametric Structured Latent Variable Modelfor Image Generation. - Xiaohui Zeng; Raquel Urtasun; Richard S. Zemel; Sanja Fidler; Renjie Liao (CoRR, 2021)

Structure-Coherent Deep Feature Learning for Robust Face Alignment. - Chunze Lin; Beier Zhu; Quan Wang; Renjie Liao; Chen Qian 006; Jiwen Lu; Jie Zhou 001 (IEEE Trans. Image Process., 2021)

Deep Learning on Graphs: Theory, Models, Algorithms and Applications. - Renjie Liao (2021)

EchoGNN: Explainable Ejection Fraction Estimation with Graph Neural Networks. - Masoud Mokhtari; Teresa Tsang; Purang Abolmaesumi; Renjie Liao (MICCAI (4), 2022)

Gaussian-Bernoulli RBMs Without Tears. - Renjie Liao; Simon Kornblith; Mengye Ren; David J. Fleet; Geoffrey E. Hinton (CoRR, 2022)

Learning Latent Part-Whole Hierarchies for Point Clouds. - Xiang Gao 014; Wei Hu 003; Renjie Liao (CoRR, 2022)

GraphPNAS: Learning Distribution of Good Neural Architectures via Deep Graph Generative Models. - Muchen Li; Jeffrey Yunfan Liu; Leonid Sigal; Renjie Liao (CoRR, 2022)

GeoNet++: Iterative Geometric Neural Network with Edge-Aware Refinement for Joint Depth and Surface Normal Estimation. - Xiaojuan Qi 001; Zhengzhe Liu; Renjie Liao; Philip H. S. Torr; Raquel Urtasun; Jiaya Jia (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Research On Tea Leaf Recognition Based On YOLOv5 Algorithm. - Mingshan Xie; Yijun Peng; Song Zhang; Jun Chen; Linfeng Liu; Hao Wang 146; Renjie Liao (ICCDA, 2023)

Specformer: Spectral Graph Neural Networks Meet Transformers. - Deyu Bo; Chuan Shi 001; Lele Wang 001; Renjie Liao (ICLR, 2023)

Scaling Forward Gradient With Local Losses. - Mengye Ren; Simon Kornblith; Renjie Liao; Geoffrey E. Hinton (ICLR, 2023)

EchoGLAD: Hierarchical Graph Neural Networks for Left Ventricle Landmark Detection on Echocardiograms. - Masoud Mokhtari; Mobina Mahdavi; Hooman Vaseli; Christina Luong 001; Purang Abolmaesumi; Teresa S. M. Tsang; Renjie Liao (MICCAI (4), 2023)

GEMTrans: A General, Echocardiography-Based, Multi-level Transformer Framework for Cardiovascular Diagnosis. - Masoud Mokhtari; Neda Ahmadi; Teresa S. M. Tsang; Purang Abolmaesumi; Renjie Liao (MLMI@MICCAI (2), 2023)

VLC-BERT: Visual Question Answering with Contextualized Commonsense Knowledge. - Sahithya Ravi; Aditya Chinchure; Leonid Sigal; Renjie Liao; Vered Shwartz (WACV, 2023)

NeuralBF: Neural Bilateral Filtering for Top-down Instance Segmentation on Point Clouds. - Weiwei Sun; Daniel Rebain; Renjie Liao; Vladimir Tankovich; Soroosh Yazdani; Kwang Moo Yi; Andrea Tagliasacchi (WACV, 2023)

GraphPNAS: Learning Probabilistic Graph Generators for Neural Architecture Search. - Muchen Li; Jeffrey Yunfan Liu; Leonid Sigal; Renjie Liao (Trans. Mach. Learn. Res., 2023)

Towards Better Out-of-Distribution Generalization of Neural Algorithmic Reasoning Tasks. - Sadegh Mahdavi; Kevin Swersky; Thomas Kipf; Milad Hashemi; Christos Thrampoulidis; Renjie Liao (Trans. Mach. Learn. Res., 2023)

Generative 3D Part Assembly via Part-Whole-Hierarchy Message Passing. - Bi'an Du; Xiang Gao; Wei Hu; Renjie Liao (CVPR, 2024)

NTIRE 2024 Quality Assessment of AI-Generated Content Challenge. - Xiaohong Liu 001; Xiongkuo Min; Guangtao Zhai; Chunyi Li; Tengchuan Kou; Wei Sun 029; Haoning Wu 001; Yixuan Gao; Yuqin Cao; Zicheng Zhang; Xiele Wu; Radu Timofte; et al. (CVPR Workshops, 2024)

Revisiting the Equivalence of In-Context Learning and Gradient Descent: The Impact of Data Distribution. - Sadegh Mahdavi; Renjie Liao; Christos Thrampoulidis (ICASSP, 2024)

Memorization Capacity of Multi-Head Attention in Transformers. - Sadegh Mahdavi; Renjie Liao; Christos Thrampoulidis (ICLR, 2024)

Learning Latent Structures in Network Games via Data-Dependent Gated-Prior Graph Variational Autoencoders. - Xue Yu; Muchen Li; Yan Leng; Renjie Liao (ICML, 2024)

An Information-Theoretic Framework for Out-of-Distribution Generalization. - Wenliang Liu 004; Guanding Yu; Lele Wang 001; Renjie Liao (ISIT, 2024)

Self-Supervised Relation Alignment for Scene Graph Generation. - Bicheng Xu; Renjie Liao; Leonid Sigal (WACV, 2024)

Joint Generative Modeling of Scene Graphs and Images via Diffusion Models. - Bicheng Xu; Qi Yan; Renjie Liao; Lele Wang 001; Leonid Sigal (CoRR, 2024)

Generative 3D Part Assembly via Part-Whole-Hierarchy Message Passing. - Bi'an Du; Xiang Gao 014; Wei Hu 003; Renjie Liao (CoRR, 2024)

Fréchet Video Motion Distance: A Metric for Evaluating Motion Consistency in Videos. - Jiahe Liu; Youran Qu; Qi Yan; Xiaohui Zeng; Lele Wang 001; Renjie Liao (CoRR, 2024)

From Graph Diffusion to Graph Classification. - Jia Jun Cheng Xian; Sadegh Mahdavi; Renjie Liao; Oliver Schulte (CoRR, 2024)

Multi-scale Latent Point Consistency Models for 3D Shape Generation. - Bi'an Du; Wei Hu 003; Renjie Liao (CoRR, 2024)

Probabilistic models for evaluating network edge's resistance against scan and foothold attack. - Shuo Wang; Qingqi Pei; Yang Xiao; Feng Shao; Shuai Yuan; Jiang Chu; Renjie Liao (IET Commun., 2024)

SwinGNN: Rethinking Permutation Invariance in Diffusion Models for Graph Generation. - Qi Yan; Zhengyang Liang; Yang Song; Renjie Liao; Lele Wang 001 (Trans. Mach. Learn. Res., 2024)

Malicious domain detection based on semi-supervised learning and parameter optimization. - Renjie Liao; Shuo Wang 025 (IET Commun., 2024)

MoFlow: One-Step Flow Matching for Human Trajectory Forecasting via Implicit Maximum Likelihood Estimation based Distillation. - Yuxiang Fu; Qi Yan; Lele Wang 001; Ke Li; Renjie Liao (CVPR, 2025)

LatentHOI: On the Generalizable Hand Object Motion Generation with Latent Hand Diffusion. - Muchen Li; Sammy Christen; Chengde Wan; Yujun Cai; Renjie Liao; Leonid Sigal; Shugao Ma (CVPR, 2025)

SymmetricDiffusers: Learning Discrete Diffusion on Finite Symmetric Groups. - Yongxing Zhang; Donglin Yang; Renjie Liao (ICLR, 2025)

Joint Generative Modeling of Grounded Scene Graphs and Images via Diffusion Models. - Bicheng Xu; Qi Yan; Renjie Liao; Lele Wang 001; Leonid Sigal (Trans. Mach. Learn. Res., 2025)

Leveraging Online Olympiad-Level Math Problems for LLMs Training and Contamination-Resistant Evaluation. - Sadegh Mahdavi; Muchen Li; Kaiwen Liu; Christos Thrampoulidis; Leonid Sigal; Renjie Liao (CoRR, 2025)

QDM: Quadtree-Based Region-Adaptive Sparse Diffusion Models for Efficient Image Super-Resolution. - Donglin Yang; Paul Vicol; Xiaojuan Qi 001; Renjie Liao; Xiaofan Zhang (CoRR, 2025)

UniEdit-Flow: Unleashing Inversion and Editing in the Era of Flow Models. - Guanlong Jiao; Biqing Huang; Kuan-Chieh Wang; Renjie Liao (CoRR, 2025)

RETRO SYNFLOW: Discrete Flow Matching for Accurate and Diverse Single-Step Retrosynthesis. - Robin Yadav; Qi Yan; Guy Wolf; Avishek Joey Bose; Renjie Liao (CoRR, 2025)

Neural MJD: Neural Non-Stationary Merton Jump Diffusion for Time Series Prediction. - Yuanpei Gao; Qi Yan; Yan Leng; Renjie Liao (CoRR, 2025)

TrajFlow: Multi-modal Motion Prediction via Flow Matching. - Qi Yan; Brian Zhang; Yutong Zhang; Daniel Yang; Joshua White; Di Chen; Jiachao Liu; Langechuan Liu; Binnan Zhuang; Shaoshuai Shi; Renjie Liao (CoRR, 2025)

StreamSplat: Towards Online Dynamic 3D Reconstruction from Uncalibrated Video Streams. - Zike Wu; Qi Yan; Xuanyu Yi; Lele Wang 001; Renjie Liao (CoRR, 2025)

Score Augmentation for Diffusion Models. - Liang Hou; Yuan Gao; Boyuan Jiang; Xin Tao 001; Qi Yan; Renjie Liao; Pengfei Wan 001; Di Zhang; Kun Gai (CoRR, 2025)