Renjie Liao

ORCID

Publication Activity (10 Years)

Years Active: 2010-2024
Publications (10 Years): 104

Top Venues

Publications

Bicheng Xu, Renjie Liao, Leonid Sigal
Self-Supervised Relation Alignment for Scene Graph Generation. WACV (2024)
Bi'an Du, Xiang Gao, Wei Hu, Renjie Liao
Generative 3D Part Assembly via Part-Whole-Hierarchy Message Passing. CoRR (2024)
Wenliang Liu, Guanding Yu, Lele Wang, Renjie Liao
An Information-Theoretic Framework for Out-of-Distribution Generalization. CoRR (2024)
Renjie Liao, Shuo Wang
Malicious domain detection based on semi-supervised learning and parameter optimization. IET Commun. 18 (6) (2024)
Jiahe Liu, Youran Qu, Qi Yan, Xiaohui Zeng, Lele Wang, Renjie Liao
Fréchet Video Motion Distance: A Metric for Evaluating Motion Consistency in Videos. CoRR (2024)
Sadegh Mahdavi, Renjie Liao, Christos Thrampoulidis
Memorization Capacity of Multi-Head Attention in Transformers. ICLR (2024)
Qi Yan, Zhengyang Liang, Yang Song, Renjie Liao, Lele Wang
SwinGNN: Rethinking Permutation Invariance in Diffusion Models for Graph Generation. Trans. Mach. Learn. Res. 2024 (2024)
Sadegh Mahdavi, Renjie Liao, Christos Thrampoulidis
Revisiting the Equivalence of In-Context Learning and Gradient Descent: The Impact of Data Distribution. ICASSP (2024)
Bicheng Xu, Qi Yan, Renjie Liao, Lele Wang, Leonid Sigal
Joint Generative Modeling of Scene Graphs and Images via Diffusion Models. CoRR (2024)
Sadegh Mahdavi, Renjie Liao, Christos Thrampoulidis
Memorization Capacity of Multi-Head Attention in Transformers. CoRR (2023)
Mengye Ren, Simon Kornblith, Renjie Liao, Geoffrey E. Hinton
Scaling Forward Gradient With Local Losses. ICLR (2023)
Muchen Li, Jeffrey Yunfan Liu, Leonid Sigal, Renjie Liao
GraphPNAS: Learning Probabilistic Graph Generators for Neural Architecture Search. Trans. Mach. Learn. Res. 2023 (2023)
Masoud Mokhtari, Mobina Mahdavi, Hooman Vaseli, Christina Luong, Purang Abolmaesumi, Teresa S. M. Tsang, Renjie Liao
EchoGLAD: Hierarchical Graph Neural Networks for Left Ventricle Landmark Detection on Echocardiograms. CoRR (2023)
Masoud Mokhtari, Neda Ahmadi, Teresa S. M. Tsang, Purang Abolmaesumi, Renjie Liao
GEMTrans: A General, Echocardiography-Based, Multi-level Transformer Framework for Cardiovascular Diagnosis. MLMI@MICCAI (2) (2023)
Mingshan Xie, Yijun Peng, Song Zhang, Jun Chen, Linfeng Liu, Hao Wang, Renjie Liao
Research On Tea Leaf Recognition Based On YOLOv5 Algorithm. ICCDA (2023)
Masoud Mokhtari, Mobina Mahdavi, Hooman Vaseli, Christina Luong, Purang Abolmaesumi, Teresa S. M. Tsang, Renjie Liao
EchoGLAD: Hierarchical Graph Neural Networks for Left Ventricle Landmark Detection on Echocardiograms. MICCAI (4) (2023)
Sahithya Ravi, Aditya Chinchure, Leonid Sigal, Renjie Liao, Vered Shwartz
VLC-BERT: Visual Question Answering with Contextualized Commonsense Knowledge. WACV (2023)
Sadegh Mahdavi, Kevin Swersky, Thomas Kipf, Milad Hashemi, Christos Thrampoulidis, Renjie Liao
Towards Better Out-of-Distribution Generalization of Neural Algorithmic Reasoning Tasks. Trans. Mach. Learn. Res. 2023 (2023)
Qi Yan, Zhengyang Liang, Yang Song, Renjie Liao, Lele Wang
SwinGNN: Rethinking Permutation Invariance in Diffusion Models for Graph Generation. CoRR (2023)
Weiwei Sun, Daniel Rebain, Renjie Liao, Vladimir Tankovich, Soroosh Yazdani, Kwang Moo Yi, Andrea Tagliasacchi
NeuralBF: Neural Bilateral Filtering for Top-down Instance Segmentation on Point Clouds. WACV (2023)
Deyu Bo, Chuan Shi, Lele Wang, Renjie Liao
Specformer: Spectral Graph Neural Networks Meet Transformers. CoRR (2023)
Bicheng Xu, Renjie Liao, Leonid Sigal
Self-Supervised Relation Alignment for Scene Graph Generation. CoRR (2023)
Masoud Mokhtari, Neda Ahmadi, Teresa S. M. Tsang, Purang Abolmaesumi, Renjie Liao
GEMTrans: A General, Echocardiography-based, Multi-Level Transformer Framework for Cardiovascular Diagnosis. CoRR (2023)
Deyu Bo, Chuan Shi, Lele Wang, Renjie Liao
Specformer: Spectral Graph Neural Networks Meet Transformers. ICLR (2023)
Muchen Li, Jeffrey Yunfan Liu, Leonid Sigal, Renjie Liao
GraphPNAS: Learning Distribution of Good Neural Architectures via Deep Graph Generative Models. CoRR (2022)
Sadegh Mahdavi, Kevin Swersky, Thomas Kipf, Milad Hashemi, Christos Thrampoulidis, Renjie Liao
Towards Better Out-of-Distribution Generalization of Neural Algorithmic Reasoning Tasks. CoRR (2022)
Renjie Liao, Simon Kornblith, Mengye Ren, David J. Fleet, Geoffrey E. Hinton
Gaussian-Bernoulli RBMs Without Tears. CoRR (2022)
Xiaojuan Qi, Zhengzhe Liu, Renjie Liao, Philip H. S. Torr, Raquel Urtasun, Jiaya Jia
GeoNet++: Iterative Geometric Neural Network with Edge-Aware Refinement for Joint Depth and Surface Normal Estimation. IEEE Trans. Pattern Anal. Mach. Intell. 44 (2) (2022)
Xiang Gao, Wei Hu, Renjie Liao
Learning Latent Part-Whole Hierarchies for Point Clouds. CoRR (2022)
Masoud Mokhtari, Teresa Tsang, Purang Abolmaesumi, Renjie Liao
EchoGNN: Explainable Ejection Fraction Estimation with Graph Neural Networks. CoRR (2022)
Mengye Ren, Simon Kornblith, Renjie Liao, Geoffrey E. Hinton
Scaling Forward Gradient With Local Losses. CoRR (2022)
Sahithya Ravi, Aditya Chinchure, Leonid Sigal, Renjie Liao, Vered Shwartz
VLC-BERT: Visual Question Answering with Contextualized Commonsense Knowledge. CoRR (2022)
Weiwei Sun, Daniel Rebain, Renjie Liao, Vladimir Tankovich, Soroosh Yazdani, Kwang Moo Yi, Andrea Tagliasacchi
NeuralBF: Neural Bilateral Filtering for Top-down Instance Segmentation on Point Clouds. CoRR (2022)
Masoud Mokhtari, Teresa Tsang, Purang Abolmaesumi, Renjie Liao
EchoGNN: Explainable Ejection Fraction Estimation with Graph Neural Networks. MICCAI (4) (2022)
Xiaohui Zeng, Raquel Urtasun, Richard S. Zemel, Sanja Fidler, Renjie Liao
NP-DRAW: A Non-Parametric Structured Latent Variable Model for Image Generation. UAI (2021)
Katie Luo, Sergio Casas, Renjie Liao, Xinchen Yan, Yuwen Xiong, Wenyuan Zeng, Raquel Urtasun
Safety-Oriented Pedestrian Occupancy Forecasting. IROS (2021)
Yang Song, Chenlin Meng, Renjie Liao, Stefano Ermon
Accelerating Feedforward Computation via Parallel Nonlinear Equation Solving. ICML (2021)
Alexander Cui, Abbas Sadat, Sergio Casas, Renjie Liao, Raquel Urtasun
LookOut: Diverse Multi-Future Prediction and Planning for Self-Driving. CoRR (2021)
Renjie Liao, Raquel Urtasun, Richard S. Zemel
A PAC-Bayesian Approach to Generalization Bounds for Graph Neural Networks. ICLR (2021)
Xin Zhang, Weixiao Huang, Yanhua Li, Renjie Liao, Ziming Zhang
Imitation Learning From Inconcurrent Multi-Agent Interactions. CDC (2021)
Wenyuan Zeng, Ming Liang, Renjie Liao, Raquel Urtasun
LaneRCNN: Distributed Representations for Graph-Centric Motion Forecasting. CoRR (2021)
Alexander Cui, Sergio Casas, Abbas Sadat, Renjie Liao, Raquel Urtasun
LookOut: Diverse Multi-Future Prediction and Planning for Self-Driving. ICCV (2021)
Chunze Lin, Beier Zhu, Quan Wang, Renjie Liao, Chen Qian, Jiwen Lu, Jie Zhou
Structure-Coherent Deep Feature Learning for Robust Face Alignment. IEEE Trans. Image Process. 30 (2021)
Wenyuan Zeng, Ming Liang, Renjie Liao, Raquel Urtasun
LaneRCNN: Distributed Representations for Graph-Centric Motion Forecasting. IROS (2021)
Katie Luo, Sergio Casas, Renjie Liao, Xinchen Yan, Yuwen Xiong, Wenyuan Zeng, Raquel Urtasun
Safety-Oriented Pedestrian Motion and Scene Occupancy Forecasting. CoRR (2021)
Xiaohui Zeng, Raquel Urtasun, Richard S. Zemel, Sanja Fidler, Renjie Liao
NP-DRAW: A Non-Parametric Structured Latent Variable Modelfor Image Generation. CoRR (2021)
Beier Zhu, Chunze Lin, Quan Wang, Renjie Liao, Chen Qian
Fast and Accurate: Structure Coherence Component for Face Alignment. CoRR (2020)
Kelvin Wong, Qiang Zhang, Ming Liang, Bin Yang, Renjie Liao, Abbas Sadat, Raquel Urtasun
Testing the Safety of Self-driving Vehicles by Simulating Perception and Prediction. CoRR (2020)
Yang Song, Chenlin Meng, Renjie Liao, Stefano Ermon
Nonlinear Equation Solving: A Faster Alternative to Feedforward Computation. CoRR (2020)
Avishek Joey Bose, Ariella Smofsky, Renjie Liao, Prakash Panangaden, William L. Hamilton
Latent Variable Modelling with Hyperbolic Normalizing Flows. ICML (2020)
Wenyuan Zeng, Shenlong Wang, Renjie Liao, Yun Chen, Bin Yang, Raquel Urtasun
DSDNet: Deep Structured Self-driving Network. ECCV (21) (2020)
Sergio Casas, Cole Gulino, Simon Suo, Katie Luo, Renjie Liao, Raquel Urtasun
Implicit Latent Variable Model for Scene-Consistent Motion Forecasting. CoRR (2020)
Wenyuan Zeng, Shenlong Wang, Renjie Liao, Yun Chen, Bin Yang, Raquel Urtasun
DSDNet: Deep Structured self-Driving Network. CoRR (2020)
Kelvin Wong, Qiang Zhang, Ming Liang, Bin Yang, Renjie Liao, Abbas Sadat, Raquel Urtasun
Testing the Safety of Self-driving Vehicles by Simulating Perception and Prediction. ECCV (26) (2020)
Sergio Casas, Cole Gulino, Renjie Liao, Raquel Urtasun
SpAGNN: Spatially-Aware Graph Neural Networks for Relational Behavior Forecasting from Sensor Data. ICRA (2020)
Avishek Joey Bose, Ariella Smofsky, Renjie Liao, Prakash Panangaden, William L. Hamilton
Latent Variable Modelling with Hyperbolic Normalizing Flows. CoRR (2020)
Ming Liang, Bin Yang, Rui Hu, Yun Chen, Renjie Liao, Song Feng, Raquel Urtasun
Learning Lane Graph Representations for Motion Forecasting. CoRR (2020)
Renjie Liao, Raquel Urtasun, Richard S. Zemel
A PAC-Bayesian Approach to Generalization Bounds for Graph Neural Networks. CoRR (2020)
Ming Liang, Bin Yang, Rui Hu, Yun Chen, Renjie Liao, Song Feng, Raquel Urtasun
Learning Lane Graph Representations for Motion Forecasting. ECCV (2) (2020)
Sergio Casas, Cole Gulino, Simon Suo, Katie Luo, Renjie Liao, Raquel Urtasun
Implicit Latent Variable Model for Scene-Consistent Motion Forecasting. ECCV (23) (2020)
Xiaojuan Qi, Zhengzhe Liu, Renjie Liao, Philip H. S. Torr, Raquel Urtasun, Jiaya Jia
GeoNet++: Iterative Geometric Neural Network with Edge-Aware Refinement for Joint Depth and Surface Normal Estimation. CoRR (2020)
Renjie Liao, Zhizhen Zhao, Raquel Urtasun, Richard S. Zemel
LanczosNet: Multi-Scale Deep Graph Convolutional Networks. ICLR (Poster) (2019)
Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, Charlie Nash, William L. Hamilton, David Duvenaud, Raquel Urtasun, Richard S. Zemel
Efficient Graph Generation with Graph Recurrent Attention Networks. CoRR (2019)
Xiaohui Zeng, Renjie Liao, Li Gu, Yuwen Xiong, Sanja Fidler, Raquel Urtasun
DMM-Net: Differentiable Mask-Matching Network for Video Object Segmentation. CoRR (2019)
Xiaohui Zeng, Renjie Liao, Li Gu, Yuwen Xiong, Sanja Fidler, Raquel Urtasun
DMM-Net: Differentiable Mask-Matching Network for Video Object Segmentation. ICCV (2019)
Dominic Cheng, Renjie Liao, Sanja Fidler, Raquel Urtasun
DARNet: Deep Active Ray Network for Building Segmentation. CVPR (2019)
Ajay Jain, Sergio Casas, Renjie Liao, Yuwen Xiong, Song Feng, Sean Segal, Raquel Urtasun
Discrete Residual Flow for Probabilistic Pedestrian Behavior Prediction. CoRR (2019)
Dominic Cheng, Renjie Liao, Sanja Fidler, Raquel Urtasun
DARNet: Deep Active Ray Network for Building Segmentation. CoRR (2019)
Mengye Ren, Renjie Liao, Ethan Fetaya, Richard S. Zemel
Incremental Few-Shot Learning with Attention Attractor Networks. NeurIPS (2019)
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
Alchemy: A Quantum Chemistry Dataset for Benchmarking AI Models. CoRR (2019)
KiJung Yoon, Renjie Liao, Yuwen Xiong, Lisa Zhang, Ethan Fetaya, Raquel Urtasun, Richard S. Zemel, Xaq Pitkow
Inference in Probabilistic Graphical Models by Graph Neural Networks. ACSSC (2019)
Marc Teva Law, Renjie Liao, Jake Snell, Richard S. Zemel
Lorentzian Distance Learning for Hyperbolic Representations. ICML (2019)
Renjie Liao, Zhizhen Zhao, Raquel Urtasun, Richard S. Zemel
LanczosNet: Multi-Scale Deep Graph Convolutional Networks. CoRR (2019)
Yuwen Xiong, Renjie Liao, Hengshuang Zhao, Rui Hu, Min Bai, Ersin Yumer, Raquel Urtasun
UPSNet: A Unified Panoptic Segmentation Network. CoRR (2019)
Sergio Casas, Cole Gulino, Renjie Liao, Raquel Urtasun
Spatially-Aware Graph Neural Networks for Relational Behavior Forecasting from Sensor Data. CoRR (2019)
Yuwen Xiong, Mengye Ren, Renjie Liao, Kelvin Wong, Raquel Urtasun
Deformable Filter Convolution for Point Cloud Reasoning. CoRR (2019)
Yuwen Xiong, Renjie Liao, Hengshuang Zhao, Rui Hu, Min Bai, Ersin Yumer, Raquel Urtasun
UPSNet: A Unified Panoptic Segmentation Network. CVPR (2019)
Ajay Jain, Sergio Casas, Renjie Liao, Yuwen Xiong, Song Feng, Sean Segal, Raquel Urtasun
Discrete Residual Flow for Probabilistic Pedestrian Behavior Prediction. CoRL (2019)
Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, William L. Hamilton, David Duvenaud, Raquel Urtasun, Richard S. Zemel
Efficient Graph Generation with Graph Recurrent Attention Networks. NeurIPS (2019)
Diego Marcos, Devis Tuia, Benjamin Kellenberger, Lisa Zhang, Min Bai, Renjie Liao, Raquel Urtasun
Learning deep structured active contours end-to-end. CoRR (2018)
Xiaojuan Qi, Renjie Liao, Zhengzhe Liu, Raquel Urtasun, Jiaya Jia
GeoNet: Geometric Neural Network for Joint Depth and Surface Normal Estimation. CVPR (2018)
KiJung Yoon, Renjie Liao, Yuwen Xiong, Lisa Zhang, Ethan Fetaya, Raquel Urtasun, Richard S. Zemel, Xaq Pitkow
Inference in probabilistic graphical models by Graph Neural Networks. ICLR (Workshop) (2018)
Yuhuai Wu, Mengye Ren, Renjie Liao, Roger B. Grosse
Understanding Short-Horizon Bias in Stochastic Meta-Optimization. ICLR (Poster) (2018)
Renjie Liao, Marc Brockschmidt, Daniel Tarlow, Alexander L. Gaunt, Raquel Urtasun, Richard S. Zemel
Graph Partition Neural Networks for Semi-Supervised Classification. ICLR (Workshop) (2018)
Lisa Zhang, Gregory Rosenblatt, Ethan Fetaya, Renjie Liao, William E. Byrd, Matthew Might, Raquel Urtasun, Richard S. Zemel
Neural Guided Constraint Logic Programming for Program Synthesis. NeurIPS (2018)
Renjie Liao, Marc Brockschmidt, Daniel Tarlow, Alexander L. Gaunt, Raquel Urtasun, Richard S. Zemel
Graph Partition Neural Networks for Semi-Supervised Classification. CoRR (2018)
KiJung Yoon, Renjie Liao, Yuwen Xiong, Lisa Zhang, Ethan Fetaya, Raquel Urtasun, Richard S. Zemel, Xaq Pitkow
Inference in Probabilistic Graphical Models by Graph Neural Networks. CoRR (2018)
Yuhuai Wu, Mengye Ren, Renjie Liao, Roger B. Grosse
Understanding Short-Horizon Bias in Stochastic Meta-Optimization. CoRR (2018)
Diego Marcos, Devis Tuia, Benjamin Kellenberger, Lisa Zhang, Min Bai, Renjie Liao, Raquel Urtasun
Learning Deep Structured Active Contours End-to-End. CVPR (2018)
Renjie Liao, Yuwen Xiong, Ethan Fetaya, Lisa Zhang, KiJung Yoon, Xaq Pitkow, Raquel Urtasun, Richard S. Zemel
Reviving and Improving Recurrent Back-Propagation. ICML (2018)
Lisa Zhang, Gregory Rosenblatt, Ethan Fetaya, Renjie Liao, William E. Byrd, Matthew Might, Raquel Urtasun, Richard S. Zemel
Neural Guided Constraint Logic Programming for Program Synthesis. CoRR (2018)
Mengye Ren, Renjie Liao, Ethan Fetaya, Richard S. Zemel
Incremental Few-Shot Learning with Attention Attractor Networks. CoRR (2018)
Lisa Zhang, Gregory Rosenblatt, Ethan Fetaya, Renjie Liao, William E. Byrd, Raquel Urtasun, Richard S. Zemel
Leveraging Constraint Logic Programming for Neural Guided Program Synthesis. ICLR (Workshop) (2018)
Renjie Liao, Yuwen Xiong, Ethan Fetaya, Lisa Zhang, KiJung Yoon, Xaq Pitkow, Raquel Urtasun, Richard S. Zemel
Reviving and Improving Recurrent Back-Propagation. CoRR (2018)
Tingwu Wang, Renjie Liao, Jimmy Ba, Sanja Fidler
NerveNet: Learning Structured Policy with Graph Neural Networks. ICLR (Poster) (2018)
Xin Tao, Hongyun Gao, Renjie Liao, Jue Wang, Jiaya Jia
Detail-revealing Deep Video Super-resolution. CoRR (2017)
Ruiyu Li, Makarand Tapaswi, Renjie Liao, Jiaya Jia, Raquel Urtasun, Sanja Fidler
Situation Recognition with Graph Neural Networks. ICCV (2017)
Jake Snell, Karl Ridgeway, Renjie Liao, Brett D. Roads, Michael C. Mozer, Richard S. Zemel
Learning to generate images with perceptual similarity metrics. ICIP (2017)
Ruiyu Li, Makarand Tapaswi, Renjie Liao, Jiaya Jia, Raquel Urtasun, Sanja Fidler
Situation Recognition with Graph Neural Networks. CoRR (2017)
Mengye Ren, Renjie Liao, Raquel Urtasun, Fabian H. Sinz, Richard S. Zemel
Normalizing the Normalizers: Comparing and Extending Network Normalization Schemes. ICLR (Poster) (2017)

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Publication Activity (10 Years)

Top Topics

Top Venues

Renjie Liao