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2020 – today
- 2024
[j7]Renjie Liao
, Shuo Wang:
Malicious domain detection based on semi-supervised learning and parameter optimization. IET Commun. 18(6): 386-397 (2024)- [j6]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) - [c64]Xiaohong Liu, Xiongkuo Min, Guangtao Zhai, Chunyi Li, Tengchuan Kou, Wei Sun, Haoning Wu, Yixuan Gao, Yuqin Cao, Zicheng Zhang, Xiele Wu, Radu Timofte, Fei Peng, Huiyuan Fu, Anlong Ming, Chuanming Wang, Huadong Ma, Shuai He, Zifei Dou, Shu Chen, Huacong Zhang, Haiyi Xie, Chengwei Wang, Baoying Chen, Jishen Zeng, Jianquan Yang, Weigang Wang, Xi Fang, Xiaoxin Lv, Jun Yan, Tianwu Zhi, Yabin Zhang, Yaohui Li, Yang Li, Jingwen Xu, Jianzhao Liu, Yiting Liao, Junlin Li, Zihao Yu, Fengbin Guan, Yiting Lu, Xin Li, Hossein Motamednia, S. Farhad Hosseini-Benvidi, Ahmad Mahmoudi-Aznaveh, Azadeh Mansouri, Ganzorig Gankhuyag, Kihwan Yoon, Yifang Xu, Haotian Fan, Fangyuan Kong, Shiling Zhao, Weifeng Dong, Haibing Yin, Li Zhu, Zhiling Wang, Bingchen Huang, Avinab Saha, Sandeep Mishra, Shashank Gupta, Rajesh Sureddi, Oindrila Saha, Luigi Celona, Simone Bianco, Paolo Napoletano, Raimondo Schettini, Junfeng Yang, Jing Fu, Wei Zhang, Wenzhi Cao, Limei Liu, Han Peng, Weijun Yuan, Zhan Li, Yihang Cheng, Yifan Deng, Haohui Li, Bowen Qu, Yao Li, Shuqing Luo, Shunzhou Wang, Wei Gao, Zihao Lu, Marcos V. Conde, Radu Timofte, Xinrui Wang, Zhibo Chen, Ruling Liao, Yan Ye, Qiulin Wang, Bing Li, Zhaokun Zhou, Miao Geng, Rui Chen, Xin Tao, Xiaoyu Liang, Shangkun Sun, Xingyuan Ma, Jiaze Li, Mengduo Yang, Haoran Xu, Jie Zhou, Shiding Zhu, Bohan Yu, Pengfei Chen, Xinrui Xu, Jiabin Shen, Zhichao Duan, Erfan Asadi, Jiahe Liu, Qi Yan, Youran Qu, Xiaohui Zeng, Lele Wang, Renjie Liao:
NTIRE 2024 Quality Assessment of AI-Generated Content Challenge. CVPR Workshops 2024: 6337-6362 - [c63]Bi'an Du, Xiang Gao, Wei Hu, Renjie Liao:
Generative 3D Part Assembly via Part-Whole-Hierarchy Message Passing. CVPR 2024: 20850-20859 - [c62]Sadegh Mahdavi, Renjie Liao, Christos Thrampoulidis:
Revisiting the Equivalence of In-Context Learning and Gradient Descent: The Impact of Data Distribution. ICASSP 2024: 7410-7414 - [c61]Sadegh Mahdavi, Renjie Liao, Christos Thrampoulidis:
Memorization Capacity of Multi-Head Attention in Transformers. ICLR 2024 - [c60]Xue Yu, Muchen Li, Yan Leng, Renjie Liao:
Learning Latent Structures in Network Games via Data-Dependent Gated-Prior Graph Variational Autoencoders. ICML 2024 - [c59]Wenliang Liu, Guanding Yu, Lele Wang, Renjie Liao:
An Information-Theoretic Framework for Out-of-Distribution Generalization. ISIT 2024: 2670-2675 - [c58]Bicheng Xu, Renjie Liao, Leonid Sigal:
Self-Supervised Relation Alignment for Scene Graph Generation. WACV 2024: 1328-1338 - [i52]Bicheng Xu, Qi Yan, Renjie Liao, Lele Wang, Leonid Sigal:
Joint Generative Modeling of Scene Graphs and Images via Diffusion Models. CoRR abs/2401.01130 (2024) - [i51]Bi'an Du, Xiang Gao, Wei Hu, Renjie Liao:
Generative 3D Part Assembly via Part-Whole-Hierarchy Message Passing. CoRR abs/2402.17464 (2024) - [i50]Wenliang Liu, Guanding Yu, Lele Wang, Renjie Liao:
An Information-Theoretic Framework for Out-of-Distribution Generalization. CoRR abs/2403.19895 (2024) - [i49]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 abs/2407.16124 (2024) - [i48]Yongxing Zhang, Donglin Yang, Renjie Liao:
SymmetricDiffusers: Learning Discrete Diffusion on Finite Symmetric Groups. CoRR abs/2410.02942 (2024) - 2023
- [j5]Muchen Li, Jeffrey Yunfan Liu, Leonid Sigal, Renjie Liao:
GraphPNAS: Learning Probabilistic Graph Generators for Neural Architecture Search. Trans. Mach. Learn. Res. 2023 (2023) - [j4]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) - [c57]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: 100-105 - [c56]Deyu Bo, Chuan Shi, Lele Wang, Renjie Liao:
Specformer: Spectral Graph Neural Networks Meet Transformers. ICLR 2023 - [c55]Mengye Ren, Simon Kornblith, Renjie Liao, Geoffrey E. Hinton:
Scaling Forward Gradient With Local Losses. ICLR 2023 - [c54]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: 227-237 - [c53]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: 1-10 - [c52]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: 551-560 - [c51]Sahithya Ravi, Aditya Chinchure, Leonid Sigal, Renjie Liao, Vered Shwartz:
VLC-BERT: Visual Question Answering with Contextualized Commonsense Knowledge. WACV 2023: 1155-1165 - [i47]Bicheng Xu, Renjie Liao, Leonid Sigal:
Self-Supervised Relation Alignment for Scene Graph Generation. CoRR abs/2302.01403 (2023) - [i46]Deyu Bo, Chuan Shi, Lele Wang, Renjie Liao:
Specformer: Spectral Graph Neural Networks Meet Transformers. CoRR abs/2303.01028 (2023) - [i45]Sadegh Mahdavi, Renjie Liao, Christos Thrampoulidis:
Memorization Capacity of Multi-Head Attention in Transformers. CoRR abs/2306.02010 (2023) - [i44]Qi Yan, Zhengyang Liang, Yang Song, Renjie Liao, Lele Wang:
SwinGNN: Rethinking Permutation Invariance in Diffusion Models for Graph Generation. CoRR abs/2307.01646 (2023) - [i43]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 abs/2307.12229 (2023) - [i42]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 abs/2308.13217 (2023) - 2022
- [j3]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): 969-984 (2022) - [c50]Masoud Mokhtari, Teresa Tsang, Purang Abolmaesumi, Renjie Liao:
EchoGNN: Explainable Ejection Fraction Estimation with Graph Neural Networks. MICCAI (4) 2022: 360-369 - [i41]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 abs/2207.09978 (2022) - [i40]Masoud Mokhtari, Teresa Tsang, Purang Abolmaesumi, Renjie Liao:
EchoGNN: Explainable Ejection Fraction Estimation with Graph Neural Networks. CoRR abs/2208.14003 (2022) - [i39]Mengye Ren, Simon Kornblith, Renjie Liao, Geoffrey E. Hinton:
Scaling Forward Gradient With Local Losses. CoRR abs/2210.03310 (2022) - [i38]Renjie Liao, Simon Kornblith, Mengye Ren, David J. Fleet, Geoffrey E. Hinton:
Gaussian-Bernoulli RBMs Without Tears. CoRR abs/2210.10318 (2022) - [i37]Sahithya Ravi, Aditya Chinchure, Leonid Sigal, Renjie Liao, Vered Shwartz:
VLC-BERT: Visual Question Answering with Contextualized Commonsense Knowledge. CoRR abs/2210.13626 (2022) - [i36]Sadegh Mahdavi, Kevin Swersky, Thomas Kipf, Milad Hashemi, Christos Thrampoulidis, Renjie Liao:
Towards Better Out-of-Distribution Generalization of Neural Algorithmic Reasoning Tasks. CoRR abs/2211.00692 (2022) - [i35]Xiang Gao, Wei Hu, Renjie Liao:
Learning Latent Part-Whole Hierarchies for Point Clouds. CoRR abs/2211.07082 (2022) - [i34]Muchen Li, Jeffrey Yunfan Liu, Leonid Sigal, Renjie Liao:
GraphPNAS: Learning Distribution of Good Neural Architectures via Deep Graph Generative Models. CoRR abs/2211.15155 (2022) - 2021
- [b1]Renjie Liao:
Deep Learning on Graphs: Theory, Models, Algorithms and Applications. University of Toronto, Canada, 2021 - [j2]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: 5313-5326 (2021) - [c49]Xin Zhang, Weixiao Huang, Yanhua Li, Renjie Liao, Ziming Zhang:
Imitation Learning From Inconcurrent Multi-Agent Interactions. CDC 2021: 43-48 - [c48]Alexander Cui, Sergio Casas, Abbas Sadat, Renjie Liao, Raquel Urtasun:
LookOut: Diverse Multi-Future Prediction and Planning for Self-Driving. ICCV 2021: 16087-16096 - [c47]Renjie Liao, Raquel Urtasun, Richard S. Zemel:
A PAC-Bayesian Approach to Generalization Bounds for Graph Neural Networks. ICLR 2021 - [c46]Yang Song, Chenlin Meng, Renjie Liao, Stefano Ermon:
Accelerating Feedforward Computation via Parallel Nonlinear Equation Solving. ICML 2021: 9791-9800 - [c45]Wenyuan Zeng, Ming Liang, Renjie Liao, Raquel Urtasun:
LaneRCNN: Distributed Representations for Graph-Centric Motion Forecasting. IROS 2021: 532-539 - [c44]Katie Luo, Sergio Casas, Renjie Liao, Xinchen Yan, Yuwen Xiong, Wenyuan Zeng, Raquel Urtasun:
Safety-Oriented Pedestrian Occupancy Forecasting. IROS 2021: 1015-1022 - [c43]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: 1089-1099 - [i33]Katie Luo, Sergio Casas, Renjie Liao, Xinchen Yan, Yuwen Xiong, Wenyuan Zeng, Raquel Urtasun:
Safety-Oriented Pedestrian Motion and Scene Occupancy Forecasting. CoRR abs/2101.02385 (2021) - [i32]Alexander Cui, Abbas Sadat, Sergio Casas, Renjie Liao, Raquel Urtasun:
LookOut: Diverse Multi-Future Prediction and Planning for Self-Driving. CoRR abs/2101.06547 (2021) - [i31]Wenyuan Zeng, Ming Liang, Renjie Liao, Raquel Urtasun:
LaneRCNN: Distributed Representations for Graph-Centric Motion Forecasting. CoRR abs/2101.06653 (2021) - [i30]Xiaohui Zeng, Raquel Urtasun, Richard S. Zemel, Sanja Fidler, Renjie Liao:
NP-DRAW: A Non-Parametric Structured Latent Variable Modelfor Image Generation. CoRR abs/2106.13435 (2021) - 2020
- [c42]Wenyuan Zeng, Shenlong Wang, Renjie Liao, Yun Chen, Bin Yang, Raquel Urtasun:
DSDNet: Deep Structured Self-driving Network. ECCV (21) 2020: 156-172 - [c41]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: 312-329 - [c40]Ming Liang, Bin Yang, Rui Hu, Yun Chen, Renjie Liao, Song Feng, Raquel Urtasun:
Learning Lane Graph Representations for Motion Forecasting. ECCV (2) 2020: 541-556 - [c39]Sergio Casas, Cole Gulino, Simon Suo, Katie Luo, Renjie Liao, Raquel Urtasun:
Implicit Latent Variable Model for Scene-Consistent Motion Forecasting. ECCV (23) 2020: 624-641 - [c38]Avishek Joey Bose, Ariella Smofsky, Renjie Liao, Prakash Panangaden, William L. Hamilton:
Latent Variable Modelling with Hyperbolic Normalizing Flows. ICML 2020: 1045-1055 - [c37]Sergio Casas, Cole Gulino, Renjie Liao, Raquel Urtasun:
SpAGNN: Spatially-Aware Graph Neural Networks for Relational Behavior Forecasting from Sensor Data. ICRA 2020: 9491-9497 - [i29]Yang Song, Chenlin Meng, Renjie Liao, Stefano Ermon:
Nonlinear Equation Solving: A Faster Alternative to Feedforward Computation. CoRR abs/2002.03629 (2020) - [i28]Avishek Joey Bose, Ariella Smofsky, Renjie Liao, Prakash Panangaden, William L. Hamilton:
Latent Variable Modelling with Hyperbolic Normalizing Flows. CoRR abs/2002.06336 (2020) - [i27]Beier Zhu, Chunze Lin, Quan Wang, Renjie Liao, Chen Qian:
Fast and Accurate: Structure Coherence Component for Face Alignment. CoRR abs/2006.11697 (2020) - [i26]Sergio Casas, Cole Gulino, Simon Suo, Katie Luo, Renjie Liao, Raquel Urtasun:
Implicit Latent Variable Model for Scene-Consistent Motion Forecasting. CoRR abs/2007.12036 (2020) - [i25]Ming Liang, Bin Yang, Rui Hu, Yun Chen, Renjie Liao, Song Feng, Raquel Urtasun:
Learning Lane Graph Representations for Motion Forecasting. CoRR abs/2007.13732 (2020) - [i24]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 abs/2008.06020 (2020) - [i23]Wenyuan Zeng, Shenlong Wang, Renjie Liao, Yun Chen, Bin Yang, Raquel Urtasun:
DSDNet: Deep Structured self-Driving Network. CoRR abs/2008.06041 (2020) - [i22]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 abs/2012.06980 (2020) - [i21]Renjie Liao, Raquel Urtasun, Richard S. Zemel:
A PAC-Bayesian Approach to Generalization Bounds for Graph Neural Networks. CoRR abs/2012.07690 (2020)
2010 – 2019
- 2019
- [c36]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: 868-875 - [c35]Ajay Jain, Sergio Casas, Renjie Liao, Yuwen Xiong, Song Feng, Sean Segal, Raquel Urtasun:
Discrete Residual Flow for Probabilistic Pedestrian Behavior Prediction. CoRL 2019: 407-419 - [c34]Dominic Cheng, Renjie Liao, Sanja Fidler, Raquel Urtasun:
DARNet: Deep Active Ray Network for Building Segmentation. CVPR 2019: 7431-7439 - [c33]Yuwen Xiong, Renjie Liao, Hengshuang Zhao, Rui Hu, Min Bai, Ersin Yumer, Raquel Urtasun:
UPSNet: A Unified Panoptic Segmentation Network. CVPR 2019: 8818-8826 - [c32]Xiaohui Zeng, Renjie Liao, Li Gu, Yuwen Xiong, Sanja Fidler, Raquel Urtasun:
DMM-Net: Differentiable Mask-Matching Network for Video Object Segmentation. ICCV 2019: 3928-3937 - [c31]Renjie Liao, Zhizhen Zhao, Raquel Urtasun, Richard S. Zemel:
LanczosNet: Multi-Scale Deep Graph Convolutional Networks. ICLR (Poster) 2019 - [c30]Marc Teva Law, Renjie Liao, Jake Snell, Richard S. Zemel:
Lorentzian Distance Learning for Hyperbolic Representations. ICML 2019: 3672-3681 - [c29]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: 4257-4267 - [c28]Mengye Ren, Renjie Liao, Ethan Fetaya, Richard S. Zemel:
Incremental Few-Shot Learning with Attention Attractor Networks. NeurIPS 2019: 5276-5286 - [i20]Renjie Liao, Zhizhen Zhao, Raquel Urtasun, Richard S. Zemel:
LanczosNet: Multi-Scale Deep Graph Convolutional Networks. CoRR abs/1901.01484 (2019) - [i19]Yuwen Xiong, Renjie Liao, Hengshuang Zhao, Rui Hu, Min Bai, Ersin Yumer, Raquel Urtasun:
UPSNet: A Unified Panoptic Segmentation Network. CoRR abs/1901.03784 (2019) - [i18]Dominic Cheng, Renjie Liao, Sanja Fidler, Raquel Urtasun:
DARNet: Deep Active Ray Network for Building Segmentation. CoRR abs/1905.05889 (2019) - [i17]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 abs/1906.09427 (2019) - [i16]Yuwen Xiong, Mengye Ren, Renjie Liao, Kelvin Wong, Raquel Urtasun:
Deformable Filter Convolution for Point Cloud Reasoning. CoRR abs/1907.13079 (2019) - [i15]Xiaohui Zeng, Renjie Liao, Li Gu, Yuwen Xiong, Sanja Fidler, Raquel Urtasun:
DMM-Net: Differentiable Mask-Matching Network for Video Object Segmentation. CoRR abs/1909.12471 (2019) - [i14]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 abs/1910.00760 (2019) - [i13]Ajay Jain, Sergio Casas, Renjie Liao, Yuwen Xiong, Song Feng, Sean Segal, Raquel Urtasun:
Discrete Residual Flow for Probabilistic Pedestrian Behavior Prediction. CoRR abs/1910.08041 (2019) - [i12]Sergio Casas, Cole Gulino, Renjie Liao, Raquel Urtasun:
Spatially-Aware Graph Neural Networks for Relational Behavior Forecasting from Sensor Data. CoRR abs/1910.08233 (2019) - 2018
- [c27]Xiaojuan Qi, Renjie Liao, Zhengzhe Liu, Raquel Urtasun, Jiaya Jia:
GeoNet: Geometric Neural Network for Joint Depth and Surface Normal Estimation. CVPR 2018: 283-291 - [c26]Diego Marcos, Devis Tuia, Benjamin Kellenberger, Lisa Zhang, Min Bai, Renjie Liao, Raquel Urtasun:
Learning Deep Structured Active Contours End-to-End. CVPR 2018: 8877-8885 - [c25]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 - [c24]Tingwu Wang, Renjie Liao, Jimmy Ba, Sanja Fidler:
NerveNet: Learning Structured Policy with Graph Neural Networks. ICLR (Poster) 2018 - [c23]Yuhuai Wu, Mengye Ren, Renjie Liao, Roger B. Grosse:
Understanding Short-Horizon Bias in Stochastic Meta-Optimization. ICLR (Poster) 2018 - [c22]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 - [c21]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 - [c20]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: 3088-3097 - [c19]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: 1744-1753 - [i11]Yuhuai Wu, Mengye Ren, Renjie Liao, Roger B. Grosse:
Understanding Short-Horizon Bias in Stochastic Meta-Optimization. CoRR abs/1803.02021 (2018) - [i10]Renjie Liao, Marc Brockschmidt, Daniel Tarlow, Alexander L. Gaunt, Raquel Urtasun, Richard S. Zemel:
Graph Partition Neural Networks for Semi-Supervised Classification. CoRR abs/1803.06272 (2018) - [i9]Diego Marcos, Devis Tuia, Benjamin Kellenberger, Lisa Zhang, Min Bai, Renjie Liao, Raquel Urtasun:
Learning deep structured active contours end-to-end. CoRR abs/1803.06329 (2018) - [i8]Renjie Liao, Yuwen Xiong, Ethan Fetaya, Lisa Zhang, KiJung Yoon, Xaq Pitkow, Raquel Urtasun, Richard S. Zemel:
Reviving and Improving Recurrent Back-Propagation. CoRR abs/1803.06396 (2018) - [i7]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 abs/1803.07710 (2018) - [i6]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 abs/1809.02840 (2018) - [i5]Mengye Ren, Renjie Liao, Ethan Fetaya, Richard S. Zemel:
Incremental Few-Shot Learning with Attention Attractor Networks. CoRR abs/1810.07218 (2018) - 2017
- [c18]Ruiyu Li, Makarand Tapaswi, Renjie Liao, Jiaya Jia, Raquel Urtasun, Sanja Fidler:
Situation Recognition with Graph Neural Networks. ICCV 2017: 4183-4192 - [c17]Xin Tao, Hongyun Gao, Renjie Liao, Jue Wang, Jiaya Jia:
Detail-Revealing Deep Video Super-Resolution. ICCV 2017: 4482-4490 - [c16]Xiaojuan Qi, Renjie Liao, Jiaya Jia, Sanja Fidler, Raquel Urtasun:
3D Graph Neural Networks for RGBD Semantic Segmentation. ICCV 2017: 5209-5218 - [c15]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: 4277-4281 - [c14]Mengye Ren, Renjie Liao, Raquel Urtasun, Fabian H. Sinz, Richard S. Zemel:
Normalizing the Normalizers: Comparing and Extending Network Normalization Schemes. ICLR (Poster) 2017 - [i4]Xin Tao, Hongyun Gao, Renjie Liao, Jue Wang, Jiaya Jia:
Detail-revealing Deep Video Super-resolution. CoRR abs/1704.02738 (2017) - [i3]Ruiyu Li, Makarand Tapaswi, Renjie Liao, Jiaya Jia, Raquel Urtasun, Sanja Fidler:
Situation Recognition with Graph Neural Networks. CoRR abs/1708.04320 (2017) - 2016
- [c13]Renjie Liao, Alexander G. Schwing, Richard S. Zemel, Raquel Urtasun:
Learning Deep Parsimonious Representations. NIPS 2016: 5076-5084 - [i2]Mengye Ren, Renjie Liao, Raquel Urtasun, Fabian H. Sinz, Richard S. Zemel:
Normalizing the Normalizers: Comparing and Extending Network Normalization Schemes. CoRR abs/1611.04520 (2016) - 2015
- [j1]Cewu Lu, Renjie Liao, Jiaya Jia:
Personal object discovery in first-person videos. IEEE Trans. Image Process. 24(12): 5789-5799 (2015) - [c12]Ziyang Ma, Renjie Liao, Xin Tao, Li Xu, Jiaya Jia, Enhua Wu:
Handling motion blur in multi-frame super-resolution. CVPR 2015: 5224-5232 - [c11]Renjie Liao, Xin Tao, Ruiyu Li, Ziyang Ma, Jiaya Jia:
Video Super-Resolution via Deep Draft-Ensemble Learning. ICCV 2015: 531-539 - [c10]Xiaojuan Qi, Jianping Shi, Shu Liu, Renjie Liao, Jiaya Jia:
Semantic Segmentation with Object Clique Potential. ICCV 2015: 2587-2595 - [c9]Li Xu, Jimmy S. J. Ren, Qiong Yan, Renjie Liao, Jiaya Jia:
Deep Edge-Aware Filters. ICML 2015: 1669-1678 - [i1]Renjie Liao, Jianping Shi, Ziyang Ma, Jun Zhu, Jiaya Jia:
Bounded-Distortion Metric Learning. CoRR abs/1505.02377 (2015) - 2014
- [c8]Di Lin, Cewu Lu, Renjie Liao, Jiaya Jia:
Learning Important Spatial Pooling Regions for Scene Classification. CVPR 2014: 3726-3733 - [c7]Sina Lin, Zengchang Qin, Renjie Liao, Tao Wan:
A confidence growing model for super-resolution. ICIP 2014: 3929-3933 - [c6]Renjie Liao, Jun Zhu, Zengchang Qin:
Nonparametric bayesian upstream supervised multi-modal topic models. WSDM 2014: 493-502 - 2013
- [c5]Tao Wan, Zengchang Qin, Chenchen Zhu, Renjie Liao:
A robust fusion scheme for multifocus images using sparse features. ICASSP 2013: 1957-1961 - [c4]Jianping Shi, Renjie Liao, Jiaya Jia:
CoDeL: A Human Co-detection and Labeling Framework. ICCV 2013: 2096-2103 - 2012
- [c3]Renjie Liao, Zengchang Qin:
Image Super-Resolution Using Local Learnable Kernel Regression. ACCV (3) 2012: 349-360 - 2010
- [c2]Renjie Liao, Xueyao Wang, Lun Li, Zengchang Qin:
A novel serial crime prediction model based on Bayesian learning theory. ICMLC 2010: 1757-1762 - [c1]Yu Du, Haibin Duan, Renjie Liao, Xihua Li:
Improved Quantum Particle Swarm Optimization by Bloch Sphere. ICSI (1) 2010: 135-143
Coauthor Index
[j3] [c48] [c47] [c45] [c44] [c43] [i33] [i32] [i31] [i30] [c42] [c41] [c40] [c39] [c37] [i26] [i25] [i24] [i23] [i22] [i21] [c36] [c35] [c34] [c33] [c32] [c31] [c29] [i20] [i19] [i18] [i16] [i15] [i14] [i13] [i12] [c27] [c26] [c25] [c22] [c21] [c20] [c19] [i10] [i9] [i8] [i7] [i6] [c18] [c16] [c14] [i3] [c13] [i2]
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