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2020 – today
- 2024
[j40]Zhibo Wei, Yongze Liu, Yingnian Wu
, Wenbai Chen, Qing-Kui Li:
T-S fuzzy model based event-triggered change control for product and supply chain systems. Int. J. Syst. Sci. 55(3): 426-439 (2024)- [c101]Tianyang Zhao, Kunwar Yashraj Singh, Srikar Appalaraju, Peng Tang, Vijay Mahadevan, R. Manmatha, Ying Nian Wu:
No Head Left Behind - Multi-Head Alignment Distillation for Transformers. AAAI 2024: 7514-7524 - [c100]Qin Zhang, Dongsheng An, Tianjun Xiao, Tong He, Qingming Tang, Ying Nian Wu, Joseph Tighe, Yifan Xing:
Learning for Transductive Threshold Calibration in Open-World Recognition. CVPR 2024: 17097-17106 - [c99]Yasi Zhang, Peiyu Yu, Ying Nian Wu:
Object-Conditioned Energy-Based Attention Map Alignment in Text-to-Image Diffusion Models. ECCV (42) 2024: 55-71 - [c98]Deqian Kong, Furqan Khan, Xu Zhang, Prateek Singhal, Ying Nian Wu:
Long-Term Social Interaction Context: The Key to Egocentric Addressee Detection. ICASSP 2024: 8250-8254 - [c97]Qing Li, Yixin Zhu, Yitao Liang, Ying Nian Wu, Song-Chun Zhu, Siyuan Huang:
Neural-Symbolic Recursive Machine for Systematic Generalization. ICLR 2024 - [c96]Cheng Han, James Chenhao Liang, Qifan Wang, Majid Rabbani, Sohail A. Dianat, Raghuveer Rao, Ying Nian Wu, Dongfang Liu:
Image Translation as Diffusion Visual Programmers. ICLR 2024 - [c95]Qin Zhang, Linghan Xu, Jun Fang, Qingming Tang, Ying Nian Wu, Joseph Tighe, Yifan Xing:
Threshold-Consistent Margin Loss for Open-World Deep Metric Learning. ICLR 2024 - [c94]Yaxuan Zhu, Jianwen Xie, Ying Nian Wu, Ruiqi Gao:
Learning Energy-Based Models by Cooperative Diffusion Recovery Likelihood. ICLR 2024 - [c93]Andrew Lizarraga, Brandon Taraku, Edouardo Honig, Ying Nian Wu, Shantanu H. Joshi:
Differentiable VQ-VAE's for Robust White Matter Streamline Encodings. ISBI 2024: 1-5 - [i118]Cheng Han, James Chenhao Liang, Qifan Wang, Majid Rabbani, Sohail A. Dianat, Raghuveer Rao, Ying Nian Wu, Dongfang Liu:
Image Translation as Diffusion Visual Programmers. CoRR abs/2401.09742 (2024) - [i117]Deqian Kong, Dehong Xu, Minglu Zhao, Bo Pang, Jianwen Xie, Andrew Lizarraga, Yuhao Huang, Sirui Xie, Ying Nian Wu:
Latent Plan Transformer: Planning as Latent Variable Inference. CoRR abs/2402.04647 (2024) - [i116]Huixin Zhan, Ying Nian Wu, Zijun Zhang:
Efficient and Scalable Fine-Tune of Language Models for Genome Understanding. CoRR abs/2402.08075 (2024) - [i115]Deqian Kong, Yuhao Huang, Jianwen Xie, Edouardo Honig, Ming Xu, Shuanghong Xue, Pei Lin, Sanping Zhou, Sheng Zhong, Nanning Zheng, Ying Nian Wu:
Dual-Space Optimization: Improved Molecule Sequence Design by Latent Prompt Transformer. CoRR abs/2402.17179 (2024) - [i114]Shu Wang, Muzhi Han, Ziyuan Jiao, Zeyu Zhang, Ying Nian Wu, Song-Chun Zhu, Hangxin Liu:
LLM3: Large Language Model-based Task and Motion Planning with Motion Failure Reasoning. CoRR abs/2403.11552 (2024) - [i113]Yingshan Chang, Yasi Zhang, Zhiyuan Fang, Yingnian Wu, Yonatan Bisk, Feng Gao:
Skews in the Phenomenon Space Hinder Generalization in Text-to-Image Generation. CoRR abs/2403.16394 (2024) - [i112]Yasi Zhang, Peiyu Yu, Ying Nian Wu:
Object-Conditioned Energy-Based Attention Map Alignment in Text-to-Image Diffusion Models. CoRR abs/2404.07389 (2024) - [i111]Hengzhi He, Peiyu Yu, Junpeng Ren, Ying Nian Wu, Guang Cheng:
Watermarking Generative Tabular Data. CoRR abs/2405.14018 (2024) - [i110]Peiyu Yu, Dinghuai Zhang, Hengzhi He, Xiaojian Ma, Ruiyao Miao, Yifan Lu, Yasi Zhang, Deqian Kong, Ruiqi Gao, Jianwen Xie, Guang Cheng, Ying Nian Wu:
Latent Energy-Based Odyssey: Black-Box Optimization via Expanded Exploration in the Energy-Based Latent Space. CoRR abs/2405.16730 (2024) - [i109]Sirui Xie, Zhisheng Xiao, Diederik P. Kingma, Tingbo Hou, Ying Nian Wu, Kevin Patrick Murphy, Tim Salimans, Ben Poole, Ruiqi Gao:
EM Distillation for One-step Diffusion Models. CoRR abs/2405.16852 (2024) - [i108]Dehong Xu, Ruiqi Gao, Wen-Hao Zhang, Xue-Xin Wei, Ying Nian Wu:
An Investigation of Conformal Isometry Hypothesis for Grid Cells. CoRR abs/2405.16865 (2024) - [i107]Yunuo Chen, Tianyi Xie, Zeshun Zong, Xuan Li, Feng Gao, Yin Yang, Ying Nian Wu, Chenfanfu Jiang:
Atlas3D: Physically Constrained Self-Supporting Text-to-3D for Simulation and Fabrication. CoRR abs/2405.18515 (2024) - [i106]Yasi Zhang, Peiyu Yu, Yaxuan Zhu, Yingshan Chang, Feng Gao, Ying Nian Wu, Oscar Leong:
Flow Priors for Linear Inverse Problems via Iterative Corrupted Trajectory Matching. CoRR abs/2405.18816 (2024) - [i105]Muzhi Han, Yifeng Zhu, Song-Chun Zhu, Ying Nian Wu, Yuke Zhu:
InterPreT: Interactive Predicate Learning from Language Feedback for Generalizable Task Planning. CoRR abs/2405.19758 (2024) - [i104]Mingkai Chen, Taowen Wang, James Chenhao Liang, Chuan Liu, Chunshu Wu, Qifan Wang, Ying Nian Wu, Michael Huang, Chuang Ren, Ang Li, Tong Geng, Dongfang Liu:
Inertial Confinement Fusion Forecasting via LLMs. CoRR abs/2407.11098 (2024) - [i103]Chuan Liu, Chunshu Wu, Shihui Cao, Mingkai Chen, James Chenhao Liang, Ang Li, Michael Huang, Chuang Ren, Dongfang Liu, Ying Nian Wu, Tong Geng:
Diff-PIC: Revolutionizing Particle-In-Cell Simulation for Advancing Nuclear Fusion with Diffusion Models. CoRR abs/2408.02693 (2024) - [i102]Guangyan Sun, Mingyu Jin, Zhenting Wang, Cheng-Long Wang, Siqi Ma, Qifan Wang, Ying Nian Wu, Yongfeng Zhang, Dongfang Liu:
Visual Agents as Fast and Slow Thinkers. CoRR abs/2408.08862 (2024) - [i101]Sheng Cheng, Deqian Kong, Jianwen Xie, Kookjin Lee, Ying Nian Wu, Yezhou Yang:
Latent Space Energy-based Neural ODEs. CoRR abs/2409.03845 (2024) - [i100]Yaxuan Zhu, Zehao Dou, Haoxin Zheng, Yasi Zhang, Ying Nian Wu, Ruiqi Gao:
Think Twice Before You Act: Improving Inverse Problem Solving With MCMC. CoRR abs/2409.08551 (2024) - 2023
- [j39]Tan Hao, Ying Nian Wu, Zhang Jiaxing, Zhang Jing:
Study on a hybrid algorithm combining enhanced ant colony optimization and double improved simulated annealing via clustering in the Traveling Salesman Problem (TSP). PeerJ Comput. Sci. 9: e1609 (2023) - [j38]Yingnian Wu, Jing Zhang, Qingkui Li, Hao Tan:
Research on Real-Time Robust Optimization of Perishable Supply-Chain Systems Based on Digital Twins. Sensors 23(4): 1850 (2023) - [j37]Jing Zhang, Yingnian Wu, Qingkui Li:
Production Change Optimization Model of Nonlinear Supply Chain System under Emergencies. Sensors 23(7): 3718 (2023) - [j36]Yifei Xu, Jianwen Xie, Tianyang Zhao, Chris L. Baker, Yibiao Zhao, Ying Nian Wu:
Energy-Based Continuous Inverse Optimal Control. IEEE Trans. Neural Networks Learn. Syst. 34(12): 10563-10577 (2023) - [c92]Jiali Cui, Ying Nian Wu, Tian Han:
Learning Joint Latent Space EBM Prior Model for Multi-layer Generator. CVPR 2023: 3603-3612 - [c91]Yizhou Zhao, Yuanhong Zeng, Qian Long, Ying Nian Wu, Song-Chun Zhu:
Sim2Plan: Robot Motion Planning via Message Passing Between Simulation and Reality. FTC (1) 2023: 29-42 - [c90]Jiali Cui, Ying Nian Wu, Tian Han:
Learning Hierarchical Features with Joint Latent Space Energy-Based Prior. ICCV 2023: 2218-2227 - [c89]Qing Li, Siyuan Huang, Yining Hong, Yixin Zhu, Ying Nian Wu, Song-Chun Zhu:
A Minimalist Dataset for Systematic Generalization of Perception, Syntax, and Semantics. ICLR 2023 - [c88]Pan Lu, Liang Qiu, Kai-Wei Chang, Ying Nian Wu, Song-Chun Zhu, Tanmay Rajpurohit, Peter Clark, Ashwin Kalyan:
Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning. ICLR 2023 - [c87]Yu-Zhe Shi, Manjie Xu, John E. Hopcroft, Kun He, Joshua B. Tenenbaum, Song-Chun Zhu, Ying Nian Wu, Wenjuan Han, Yixin Zhu:
On the Complexity of Bayesian Generalization. ICML 2023: 31389-31407 - [c86]Yan Xu, Deqian Kong, Dehong Xu, Ziwei Ji, Bo Pang, Pascale Fung, Ying Nian Wu:
Diverse and Faithful Knowledge-Grounded Dialogue Generation via Sequential Posterior Inference. ICML 2023: 38518-38534 - [c85]Pan Lu, Baolin Peng, Hao Cheng, Michel Galley, Kai-Wei Chang, Ying Nian Wu, Song-Chun Zhu, Jianfeng Gao:
Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models. NeurIPS 2023 - [c84]Peiyu Yu, Yaxuan Zhu, Sirui Xie, Xiaojian Ma, Ruiqi Gao, Song-Chun Zhu, Ying Nian Wu:
Learning Energy-Based Prior Model with Diffusion-Amortized MCMC. NeurIPS 2023 - [c83]Junfeng Zuo, Xiao Liu, Ying Nian Wu, Si Wu, Wenhao Zhang:
A Recurrent Neural Circuit Mechanism of Temporal-scaling Equivariant Representation. NeurIPS 2023 - [c82]Quanshi Zhang, Xu Cheng, Xin Wang, Yu Yang, Yingnian Wu:
Network Transplanting for the Functionally Modular Architecture. PRCV (3) 2023: 69-83 - [c81]Deqian Kong, Bo Pang, Tian Han, Ying Nian Wu:
Molecule Design by Latent Space Energy-Based Modeling and Gradual Distribution Shifting. UAI 2023: 1109-1120 - [i99]Pan Lu, Baolin Peng, Hao Cheng, Michel Galley, Kai-Wei Chang, Ying Nian Wu, Song-Chun Zhu, Jianfeng Gao:
Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models. CoRR abs/2304.09842 (2023) - [i98]Qin Zhang, Dongsheng An, Tianjun Xiao, Tong He, Qingming Tang, Ying Nian Wu, Joseph Tighe, Yifan Xing:
Learning for Open-World Calibration with Graph Neural Networks. CoRR abs/2305.12039 (2023) - [i97]Yan Xu, Deqian Kong, Dehong Xu, Ziwei Ji, Bo Pang, Pascale Fung, Ying Nian Wu:
Diverse and Faithful Knowledge-Grounded Dialogue Generation via Sequential Posterior Inference. CoRR abs/2306.01153 (2023) - [i96]Jiali Cui, Ying Nian Wu, Tian Han:
Learning Joint Latent Space EBM Prior Model for Multi-layer Generator. CoRR abs/2306.06323 (2023) - [i95]Weinan Song, Yaxuan Zhu, Lei He, Yingnian Wu, Jianwen Xie:
Progressive Energy-Based Cooperative Learning for Multi-Domain Image-to-Image Translation. CoRR abs/2306.14448 (2023) - [i94]Deqian Kong, Bo Pang, Tian Han, Ying Nian Wu:
Molecule Design by Latent Space Energy-Based Modeling and Gradual Distribution Shifting. CoRR abs/2306.14902 (2023) - [i93]Qin Zhang, Linghan Xu, Qingming Tang, Jun Fang, Ying Nian Wu, Joe Tighe, Yifan Xing:
Calibration-Aware Margin Loss: Pushing the Accuracy-Calibration Consistency Pareto Frontier for Deep Metric Learning. CoRR abs/2307.04047 (2023) - [i92]Yizhou Zhao, Yuanhong Zeng, Qian Long, Ying Nian Wu, Song-Chun Zhu:
Sim2Plan: Robot Motion Planning via Message Passing between Simulation and Reality. CoRR abs/2307.07862 (2023) - [i91]Yaxuan Zhu, Jianwen Xie, Yingnian Wu, Ruiqi Gao:
Learning Energy-Based Models by Cooperative Diffusion Recovery Likelihood. CoRR abs/2309.05153 (2023) - [i90]Yuanhong Zeng, Yizhou Zhao, Ying Nian Wu:
Triple Regression for Camera Agnostic Sim2Real Robot Grasping and Manipulation Tasks. CoRR abs/2309.09017 (2023) - [i89]Peiyu Yu, Yaxuan Zhu, Sirui Xie, Xiaojian Ma, Ruiqi Gao, Song-Chun Zhu, Ying Nian Wu:
Learning Energy-Based Prior Model with Diffusion-Amortized MCMC. CoRR abs/2310.03218 (2023) - [i88]Deqian Kong, Yuhao Huang, Jianwen Xie, Ying Nian Wu:
Molecule Design by Latent Prompt Transformer. CoRR abs/2310.03253 (2023) - [i87]Yilue Qian, Peiyu Yu, Ying Nian Wu, Wei Wang, Lifeng Fan:
Learning Concept-Based Visual Causal Transition and Symbolic Reasoning for Visual Planning. CoRR abs/2310.03325 (2023) - [i86]Jiali Cui, Ying Nian Wu, Tian Han:
Learning Hierarchical Features with Joint Latent Space Energy-Based Prior. CoRR abs/2310.09604 (2023) - [i85]Dehong Xu, Ruiqi Gao, Wen-Hao Zhang, Xue-Xin Wei, Ying Nian Wu:
Conformal Normalization in Recurrent Neural Network of Grid Cells. CoRR abs/2310.19192 (2023) - [i84]Andrew Lizarraga, Brandon Taraku, Edouardo Honig, Ying Nian Wu, Shantanu H. Joshi:
Differentiable VQ-VAE's for Robust White Matter Streamline Encodings. CoRR abs/2311.06212 (2023) - [i83]Ziheng Zhou, Yingnian Wu, Song-Chun Zhu, Demetri Terzopoulos:
Aligner: One Global Token is Worth Millions of Parameters When Aligning Large Language Models. CoRR abs/2312.05503 (2023) - 2022
- [j35]Rui Yang, Yingnian Wu, Xiaolong Liu, Wenbai Chen:
GACSNet: A Lightweight Network for the Noninvasive Blood Glucose Detection. Appl. Artif. Intell. 36(1) (2022) - [j34]Xianglei Xing, Ruiqi Gao, Tian Han, Song-Chun Zhu, Ying Nian Wu:
Deformable Generator Networks: Unsupervised Disentanglement of Appearance and Geometry. IEEE Trans. Pattern Anal. Mach. Intell. 44(3): 1162-1179 (2022) - [j33]Jianwen Xie, Zilong Zheng, Ruiqi Gao, Wenguan Wang, Song-Chun Zhu, Ying Nian Wu:
Generative VoxelNet: Learning Energy-Based Models for 3D Shape Synthesis and Analysis. IEEE Trans. Pattern Anal. Mach. Intell. 44(5): 2468-2484 (2022) - [j32]Jianwen Xie, Zilong Zheng, Xiaolin Fang, Song-Chun Zhu, Ying Nian Wu:
Cooperative Training of Fast Thinking Initializer and Slow Thinking Solver for Conditional Learning. IEEE Trans. Pattern Anal. Mach. Intell. 44(8): 3957-3973 (2022) - [j31]Luyao Yuan, Xiaofeng Gao, Zilong Zheng, Mark Edmonds, Ying Nian Wu, Federico Rossano, Hongjing Lu, Yixin Zhu, Song-Chun Zhu:
In situ bidirectional human-robot value alignment. Sci. Robotics 7(68) (2022) - [c80]Ruiqi Gao, Jianwen Xie, Siyuan Huang, Yufan Ren, Song-Chun Zhu, Ying Nian Wu:
Learning V1 Simple Cells with Vector Representation of Local Content and Matrix Representation of Local Motion. AAAI 2022: 6674-6684 - [c79]Yifei Xu, Jingqiao Zhang, Ru He, Liangzhu Ge, Chao Yang, Cheng Yang, Ying Nian Wu:
SAS: Self-Augmentation Strategy for Language Model Pre-training. AAAI 2022: 11586-11594 - [c78]Cristian I. Challu, Peihong Jiang, Ying Nian Wu, Laurent Callot:
Deep Generative model with Hierarchical Latent Factors for Time Series Anomaly Detection. AISTATS 2022: 1643-1654 - [c77]Feng Gao, Qing Ping, Govind Thattai, Aishwarya N. Reganti, Ying Nian Wu, Prem Natarajan:
Transform-Retrieve-Generate: Natural Language-Centric Outside-Knowledge Visual Question Answering. CVPR 2022: 5057-5067 - [c76]Chi Zhang, Sirui Xie, Baoxiong Jia, Ying Nian Wu, Song-Chun Zhu, Yixin Zhu:
Learning Algebraic Representation for Systematic Generalization in Abstract Reasoning. ECCV (39) 2022: 692-709 - [c75]Erik Nijkamp, Ruiqi Gao, Pavel Sountsov, Srinivas Vasudevan, Bo Pang, Song-Chun Zhu, Ying Nian Wu:
MCMC Should Mix: Learning Energy-Based Model with Neural Transport Latent Space MCMC. ICLR 2022 - [c74]Peiyu Yu, Sirui Xie, Xiaojian Ma, Baoxiong Jia, Bo Pang, Ruiqi Gao, Yixin Zhu, Song-Chun Zhu, Ying Nian Wu:
Latent Diffusion Energy-Based Model for Interpretable Text Modelling. ICML 2022: 25702-25720 - [c73]Dehong Xu, Ruiqi Gao, Wen-Hao Zhang, Xue-Xin Wei, Ying Nian Wu:
Conformal Isometry of Lie Group Representation in Recurrent Network of Grid Cells. NeurReps 2022: 370-387 - [c72]Wenhao Zhang, Ying Nian Wu, Si Wu:
Translation-equivariant Representation in Recurrent Networks with a Continuous Manifold of Attractors. NeurIPS 2022 - [i82]Feng Gao, Qing Ping, Govind Thattai, Aishwarya N. Reganti, Ying Nian Wu, Prem Natarajan:
A Thousand Words Are Worth More Than a Picture: Natural Language-Centric Outside-Knowledge Visual Question Answering. CoRR abs/2201.05299 (2022) - [i81]Cristian Challu, Peihong Jiang, Ying Nian Wu, Laurent Callot:
Deep Generative model with Hierarchical Latent Factors for Time Series Anomaly Detection. CoRR abs/2202.07586 (2022) - [i80]Peiyu Yu, Sirui Xie, Xiaojian Ma, Baoxiong Jia, Bo Pang, Ruiqi Gao, Yixin Zhu, Song-Chun Zhu, Ying Nian Wu:
Latent Diffusion Energy-Based Model for Interpretable Text Modeling. CoRR abs/2206.05895 (2022) - [i79]Pan Lu, Liang Qiu, Kai-Wei Chang, Ying Nian Wu, Song-Chun Zhu, Tanmay Rajpurohit, Peter Clark, Ashwin Kalyan:
Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning. CoRR abs/2209.14610 (2022) - [i78]Qing Li, Yixin Zhu, Yitao Liang, Ying Nian Wu, Song-Chun Zhu, Siyuan Huang:
Neural-Symbolic Recursive Machine for Systematic Generalization. CoRR abs/2210.01603 (2022) - [i77]Dehong Xu, Ruiqi Gao, Wen-Hao Zhang, Xue-Xin Wei, Ying Nian Wu:
Conformal Isometry of Lie Group Representation in Recurrent Network of Grid Cells. CoRR abs/2210.02684 (2022) - [i76]Cristian Challu, Peihong Jiang, Ying Nian Wu, Laurent Callot:
SpectraNet: Multivariate Forecasting and Imputation under Distribution Shifts and Missing Data. CoRR abs/2210.12515 (2022) - [i75]Yu-Zhe Shi, Manjie Xu, John E. Hopcroft, Kun He, Joshua B. Tenenbaum, Song-Chun Zhu, Ying Nian Wu, Wenjuan Han, Yixin Zhu:
On the Complexity of Bayesian Generalization. CoRR abs/2211.11033 (2022) - 2021
- [j30]Jianwen Xie, Song-Chun Zhu, Ying Nian Wu:
Learning Energy-Based Spatial-Temporal Generative ConvNets for Dynamic Patterns. IEEE Trans. Pattern Anal. Mach. Intell. 43(2): 516-531 (2021) - [j29]Quanshi Zhang, Xin Wang, Ying Nian Wu, Huilin Zhou, Song-Chun Zhu:
Interpretable CNNs for Object Classification. IEEE Trans. Pattern Anal. Mach. Intell. 43(10): 3416-3431 (2021) - [j28]Quanshi Zhang, Xin Wang, Ruiming Cao, Ying Nian Wu, Feng Shi, Song-Chun Zhu:
Extraction of an Explanatory Graph to Interpret a CNN. IEEE Trans. Pattern Anal. Mach. Intell. 43(11): 3863-3877 (2021) - [j27]Quanshi Zhang, Jie Ren, Ge Huang, Ruiming Cao, Ying Nian Wu, Song-Chun Zhu:
Mining Interpretable AOG Representations From Convolutional Networks via Active Question Answering. IEEE Trans. Pattern Anal. Mach. Intell. 43(11): 3949-3963 (2021) - [c71]Jianwen Xie, Zilong Zheng, Xiaolin Fang, Song-Chun Zhu, Ying Nian Wu:
Learning Cycle-Consistent Cooperative Networks via Alternating MCMC Teaching for Unsupervised Cross-Domain Translation. AAAI 2021: 10430-10440 - [c70]Liang Qiu, Yuan Liang, Yizhou Zhao, Pan Lu, Baolin Peng, Zhou Yu, Ying Nian Wu, Song-Chun Zhu:
SocAoG: Incremental Graph Parsing for Social Relation Inference in Dialogues. ACL/IJCNLP (1) 2021: 658-670 - [c69]Wenjuan Han, Bo Pang, Ying Nian Wu:
Robust Transfer Learning with Pretrained Language Models through Adapters. ACL/IJCNLP (2) 2021: 854-861 - [c68]Yunqi Guo, Zhaowei Tan, Kaiyuan Chen, Songwu Lu, Ying Nian Wu:
A Model Obfuscation Approach to IoT Security. CNS 2021: 1-9 - [c67]Yaxuan Zhu, Ruiqi Gao, Siyuan Huang, Song-Chun Zhu, Ying Nian Wu:
Learning Neural Representation of Camera Pose with Matrix Representation of Pose Shift via View Synthesis. CVPR 2021: 9959-9968 - [c66]Bo Pang, Tianyang Zhao, Xu Xie, Ying Nian Wu:
Trajectory Prediction With Latent Belief Energy-Based Model. CVPR 2021: 11814-11824 - [c65]Jianwen Xie, Yifei Xu, Zilong Zheng, Song-Chun Zhu, Ying Nian Wu:
Generative PointNet: Deep Energy-Based Learning on Unordered Point Sets for 3D Generation, Reconstruction and Classification. CVPR 2021: 14976-14985 - [c64]Bo Pang, Erik Nijkamp, Tian Han, Ying Nian Wu:
Generative Text Modeling through Short Run Inference. EACL 2021: 1156-1165 - [c63]Ruiqi Gao, Yang Song, Ben Poole, Ying Nian Wu, Diederik P. Kingma:
Learning Energy-Based Models by Diffusion Recovery Likelihood. ICLR 2021 - [c62]Bo Pang, Ying Nian Wu:
Latent Space Energy-Based Model of Symbol-Vector Coupling for Text Generation and Classification. ICML 2021: 8359-8370 - [c61]Hung-Jui Huang, Kai-Chi Huang, Michal Cáp, Yibiao Zhao, Ying Nian Wu, Chris L. Baker:
Planning on a (Risk) Budget: Safe Non-Conservative Planning in Probabilistic Dynamic Environments. ICRA 2021: 10257-10263 - [c60]Xu Xie, Chi Zhang, Yixin Zhu, Ying Nian Wu, Song-Chun Zhu:
Congestion-aware Multi-agent Trajectory Prediction for Collision Avoidance. ICRA 2021: 13693-13700 - [c59]Yangzi Guo, Ying Nian Wu, Adrian Barbu:
A Study of Local Optima for Learning Feature Interactions using Neural Networks. IJCNN 2021: 1-8 - [c58]Erik Nijkamp, Bo Pang, Ying Nian Wu, Caiming Xiong:
SCRIPT: Self-Critic PreTraining of Transformers. NAACL-HLT 2021: 5196-5202 - [c57]Peiyu Yu, Sirui Xie, Xiaojian Ma, Yixin Zhu, Ying Nian Wu, Song-Chun Zhu:
Unsupervised Foreground Extraction via Deep Region Competition. NeurIPS 2021: 14264-14279 - [c56]Ruiqi Gao, Jianwen Xie, Xue-Xin Wei, Song-Chun Zhu, Ying Nian Wu:
On Path Integration of Grid Cells: Group Representation and Isotropic Scaling. NeurIPS 2021: 28623-28635 - [c55]Luyao Yuan, Dongruo Zhou, Junhong Shen, Jingdong Gao, Jeffrey L. Chen, Quanquan Gu, Ying Nian Wu, Song-Chun Zhu:
Iterative Teacher-Aware Learning. NeurIPS 2021: 29231-29245 - [i74]Sirui Xie, Xiaojian Ma, Peiyu Yu, Yixin Zhu, Ying Nian Wu, Song-Chun Zhu:
HALMA: Humanlike Abstraction Learning Meets Affordance in Rapid Problem Solving. CoRR abs/2102.11344 (2021) - [i73]Qing Li, Siyuan Huang, Yining Hong, Yixin Zhu, Ying Nian Wu, Song-Chun Zhu:
A HINT from Arithmetic: On Systematic Generalization of Perception, Syntax, and Semantics. CoRR abs/2103.01403 (2021) - [i72]Jianwen Xie, Zilong Zheng, Xiaolin Fang, Song-Chun Zhu, Ying Nian Wu:
Learning Cycle-Consistent Cooperative Networks via Alternating MCMC Teaching for Unsupervised Cross-Domain Translation. CoRR abs/2103.04285 (2021) - [i71]Xu Xie, Chi Zhang, Yixin Zhu, Ying Nian Wu, Song-Chun Zhu:
Congestion-aware Multi-agent Trajectory Prediction for Collision Avoidance. CoRR abs/2103.14231 (2021) - [i70]Yaxuan Zhu, Ruiqi Gao, Siyuan Huang, Song-Chun Zhu, Yingnian Wu:
Learning Neural Representation of Camera Pose with Matrix Representation of Pose Shift via View Synthesis. CoRR abs/2104.01508 (2021) - [i69]Bo Pang, Tianyang Zhao, Xu Xie, Ying Nian Wu:
Trajectory Prediction with Latent Belief Energy-Based Model. CoRR abs/2104.03086 (2021) - [i68]Liang Qiu, Yuan Liang, Yizhou Zhao, Pan Lu, Baolin Peng, Zhou Yu, Ying Nian Wu, Song-Chun Zhu:
SocAoG: Incremental Graph Parsing for Social Relation Inference in Dialogues. CoRR abs/2106.01006 (2021) - [i67]Bo Pang, Erik Nijkamp, Tian Han, Ying Nian Wu:
Generative Text Modeling through Short Run Inference. CoRR abs/2106.02513 (2021) - [i66]Yifei Xu, Jingqiao Zhang, Ru He, Liangzhu Ge, Chao Yang, Cheng Yang, Ying Nian Wu:
SAS: Self-Augmented Strategy for Language Model Pre-training. CoRR abs/2106.07176 (2021) - [i65]Hung-Jui Huang, Kai-Chi Huang, Michal Cáp, Yibiao Zhao, Ying Nian Wu, Chris L. Baker:
Planning on a (Risk) Budget: Safe Non-Conservative Planning in Probabilistic Dynamic Environments. CoRR abs/2106.09127 (2021) - [i64]Quanshi Zhang, Tian Han, Lixin Fan, Zhanxing Zhu, Hang Su, Ying Nian Wu, Jie Ren, Hao Zhang:
Proceedings of ICML 2021 Workshop on Theoretic Foundation, Criticism, and Application Trend of Explainable AI. CoRR abs/2107.08821 (2021) - [i63]Wenjuan Han, Bo Pang, Ying Nian Wu:
Robust Transfer Learning with Pretrained Language Models through Adapters. CoRR abs/2108.02340 (2021) - [i62]Bo Pang, Ying Nian Wu:
Latent Space Energy-Based Model of Symbol-Vector Coupling for Text Generation and Classification. CoRR abs/2108.11556 (2021) - [i61]Luyao Yuan, Dongruo Zhou, Junhong Shen, Jingdong Gao, Jeffrey L. Chen, Quanquan Gu, Ying Nian Wu, Song-Chun Zhu:
Iterative Teacher-Aware Learning. CoRR abs/2110.00137 (2021) - [i60]Peiyu Yu, Sirui Xie, Xiaojian Ma, Yixin Zhu, Ying Nian Wu, Song-Chun Zhu:
Unsupervised Foreground Extraction via Deep Region Competition. CoRR abs/2110.15497 (2021) - [i59]Chi Zhang, Sirui Xie, Baoxiong Jia, Ying Nian Wu, Song-Chun Zhu, Yixin Zhu:
Learning Algebraic Representation for Systematic Generalization in Abstract Reasoning. CoRR abs/2111.12990 (2021) - 2020
- [j26]Jianwen Xie, Yang Lu, Ruiqi Gao, Song-Chun Zhu, Ying Nian Wu:
Cooperative Training of Descriptor and Generator Networks. IEEE Trans. Pattern Anal. Mach. Intell. 42(1): 27-45 (2020) - [c54]Erik Nijkamp, Mitch Hill, Tian Han, Song-Chun Zhu, Ying Nian Wu:
On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models. AAAI 2020: 5272-5280 - [c53]Jianwen Xie, Ruiqi Gao, Zilong Zheng, Song-Chun Zhu, Ying Nian Wu:
Motion-Based Generator Model: Unsupervised Disentanglement of Appearance, Trackable and Intrackable Motions in Dynamic Patterns. AAAI 2020: 12442-12451 - [c52]Ruiqi Gao, Erik Nijkamp, Diederik P. Kingma, Zhen Xu, Andrew M. Dai, Ying Nian Wu:
Flow Contrastive Estimation of Energy-Based Models. CVPR 2020: 7515-7525 - [c51]Tian Han, Erik Nijkamp, Linqi Zhou, Bo Pang, Song-Chun Zhu, Ying Nian Wu:
Joint Training of Variational Auto-Encoder and Latent Energy-Based Model. CVPR 2020: 7975-7984 - [c50]Xianglei Xing, Tianfu Wu, Song-Chun Zhu, Ying Nian Wu:
Inducing Hierarchical Compositional Model by Sparsifying Generator Network. CVPR 2020: 14284-14293 - [c49]Erik Nijkamp, Bo Pang, Tian Han, Linqi Zhou, Song-Chun Zhu, Ying Nian Wu:
Learning Multi-layer Latent Variable Model via Variational Optimization of Short Run MCMC for Approximate Inference. ECCV (6) 2020: 361-378 - [c48]Qing Li, Siyuan Huang, Yining Hong, Yixin Chen, Ying Nian Wu, Song-Chun Zhu:
Closed Loop Neural-Symbolic Learning via Integrating Neural Perception, Grammar Parsing, and Symbolic Reasoning. ICML 2020: 5884-5894 - [c47]Yudi Sang, Xianglei Xing, Ying Nian Wu, Dan Ruan:
Imposing implicit feasibility constraints on deformable image registration using a statistical generative model. Medical Imaging: Image Processing 2020: 113132V - [c46]Bo Pang, Tian Han, Erik Nijkamp, Song-Chun Zhu, Ying Nian Wu:
Learning Latent Space Energy-Based Prior Model. NeurIPS 2020 - [i58]Jianwen Xie, Yifei Xu, Zilong Zheng, Song-Chun Zhu, Ying Nian Wu:
Generative PointNet: Energy-Based Learning on Unordered Point Sets for 3D Generation, Reconstruction and Classification. CoRR abs/2004.01301 (2020) - [i57]Yixin Zhu, Tao Gao, Lifeng Fan, Siyuan Huang, Mark Edmonds, Hangxin Liu, Feng Gao, Chi Zhang, Siyuan Qi, Ying Nian Wu, Joshua B. Tenenbaum, Song-Chun Zhu:
Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense. CoRR abs/2004.09044 (2020) - [i56]Tian Han, Erik Nijkamp, Linqi Zhou, Bo Pang, Song-Chun Zhu, Ying Nian Wu:
Joint Training of Variational Auto-Encoder and Latent Energy-Based Model. CoRR abs/2006.06059 (2020) - [i55]Qing Li, Siyuan Huang, Yining Hong, Yixin Chen, Ying Nian Wu, Song-Chun Zhu:
Closed Loop Neural-Symbolic Learning via Integrating Neural Perception, Grammar Parsing, and Symbolic Reasoning. CoRR abs/2006.06649 (2020) - [i54]Erik Nijkamp, Ruiqi Gao, Pavel Sountsov, Srinivas Vasudevan, Bo Pang, Song-Chun Zhu, Ying Nian Wu:
Learning Energy-based Model with Flow-based Backbone by Neural Transport MCMC. CoRR abs/2006.06897 (2020) - [i53]Bo Pang, Tian Han, Erik Nijkamp, Song-Chun Zhu, Ying Nian Wu:
Learning Latent Space Energy-Based Prior Model. CoRR abs/2006.08205 (2020) - [i52]Ruiqi Gao, Jianwen Xie, Song-Chun Zhu, Ying Nian Wu:
A Representational Model of Grid Cells Based on Matrix Lie Algebras. CoRR abs/2006.10259 (2020) - [i51]Bo Pang, Tian Han, Ying Nian Wu:
Learning Latent Space Energy-Based Prior Model for Molecule Generation. CoRR abs/2010.09351 (2020) - [i50]Bo Pang, Erik Nijkamp, Jiali Cui, Tian Han, Ying Nian Wu:
Semi-supervised Learning by Latent Space Energy-Based Model of Symbol-Vector Coupling. CoRR abs/2010.09359 (2020) - [i49]Ruiqi Gao, Yang Song, Ben Poole, Ying Nian Wu, Diederik P. Kingma:
Learning Energy-Based Models by Diffusion Recovery Likelihood. CoRR abs/2012.08125 (2020) - [i48]Jianwen Xie, Zilong Zheng, Ruiqi Gao, Wenguan Wang, Song-Chun Zhu, Ying Nian Wu:
Generative VoxelNet: Learning Energy-Based Models for 3D Shape Synthesis and Analysis. CoRR abs/2012.13522 (2020)
2010 – 2019
- 2019
- [j25]Yingnian Wu, Qi Yang, Xiaohang Zhou:
An improved method of optical flow using human body-following wheeled robot. Int. J. Model. Simul. Sci. Comput. 10(2): 1950003:1-1950003:12 (2019) - [j24]Tian Han, Xianglei Xing, Jiawen Wu, Ying Nian Wu:
Replicating Neuroscience Observations on ML/MF and AM Face Patches by Deep Generative Model. Neural Comput. 31(12): 2348-2367 (2019) - [j23]Mark Edmonds, Feng Gao, Hangxin Liu, Xu Xie, Siyuan Qi, Brandon Rothrock, Yixin Zhu, Ying Nian Wu, Hongjing Lu, Song-Chun Zhu:
A tale of two explanations: Enhancing human trust by explaining robot behavior. Sci. Robotics 4(37) (2019) - [c45]Jianwen Xie, Ruiqi Gao, Zilong Zheng, Song-Chun Zhu, Ying Nian Wu:
Learning Dynamic Generator Model by Alternating Back-Propagation through Time. AAAI 2019: 5498-5507 - [c44]Quanshi Zhang, Yu Yang, Haotian Ma, Ying Nian Wu:
Interpreting CNNs via Decision Trees. CVPR 2019: 6261-6270 - [c43]Tian Han, Erik Nijkamp, Xiaolin Fang, Mitch Hill, Song-Chun Zhu, Ying Nian Wu:
Divergence Triangle for Joint Training of Generator Model, Energy-Based Model, and Inferential Model. CVPR 2019: 8670-8679 - [c42]Xianglei Xing, Tian Han, Ruiqi Gao, Song-Chun Zhu, Ying Nian Wu:
Unsupervised Disentangling of Appearance and Geometry by Deformable Generator Network. CVPR 2019: 10354-10363 - [c41]Tianyang Zhao, Yifei Xu, Mathew Monfort, Wongun Choi, Chris L. Baker, Yibiao Zhao, Yizhou Wang, Ying Nian Wu:
Multi-Agent Tensor Fusion for Contextual Trajectory Prediction. CVPR 2019: 12126-12134 - [c40]Ruiqi Gao, Jianwen Xie, Song-Chun Zhu, Ying Nian Wu:
Learning Grid Cells as Vector Representation of Self-Position Coupled with Matrix Representation of Self-Motion. ICLR (Poster) 2019 - [c39]Erik Nijkamp, Mitch Hill, Song-Chun Zhu, Ying Nian Wu:
Learning Non-Convergent Non-Persistent Short-Run MCMC Toward Energy-Based Model. NeurIPS 2019: 5233-5243 - [c38]Tian Han, Yang Lu, Jiawen Wu, Xianglei Xing, Ying Nian Wu:
Learning Generator Networks for Dynamic Patterns. WACV 2019: 809-818 - [i47]Quanshi Zhang, Ying Nian Wu, Song-Chun Zhu:
Interpretable CNNs. CoRR abs/1901.02413 (2019) - [i46]Quanshi Zhang, Yu Yang, Qian Yu, Ying Nian Wu:
Network Transplanting (extended abstract). CoRR abs/1901.06978 (2019) - [i45]Quanshi Zhang, Yu Yang, Ying Nian Wu:
Unsupervised Learning of Neural Networks to Explain Neural Networks (extended abstract). CoRR abs/1901.07538 (2019) - [i44]Xianglei Xing, Song-Chun Zhu, Ying Nian Wu:
Inducing Sparse Coding and And-Or Grammar from Generator Network. CoRR abs/1901.11494 (2019) - [i43]Jianwen Xie, Zilong Zheng, Xiaolin Fang, Song-Chun Zhu, Ying Nian Wu:
Multimodal Conditional Learning with Fast Thinking Policy-like Model and Slow Thinking Planner-like Model. CoRR abs/1902.02812 (2019) - [i42]Ruiqi Gao, Jianwen Xie, Song-Chun Zhu, Ying Nian Wu:
Learning Vector Representation of Content and Matrix Representation of Change: Towards a Representational Model of V1. CoRR abs/1902.03871 (2019) - [i41]Erik Nijkamp, Mitch Hill, Tian Han, Song-Chun Zhu, Ying Nian Wu:
On the Anatomy of MCMC-based Maximum Likelihood Learning of Energy-Based Models. CoRR abs/1903.12370 (2019) - [i40]Tianyang Zhao, Yifei Xu, Mathew Monfort, Wongun Choi, Chris L. Baker, Yibiao Zhao, Yizhou Wang, Ying Nian Wu:
Multi-Agent Tensor Fusion for Contextual Trajectory Prediction. CoRR abs/1904.04776 (2019) - [i39]Yifei Xu, Tianyang Zhao, Chris L. Baker, Yibiao Zhao, Ying Nian Wu:
Learning Trajectory Prediction with Continuous Inverse Optimal Control via Langevin Sampling of Energy-Based Models. CoRR abs/1904.05453 (2019) - [i38]Erik Nijkamp, Song-Chun Zhu, Ying Nian Wu:
On Learning Non-Convergent Short-Run MCMC Toward Energy-Based Model. CoRR abs/1904.09770 (2019) - [i37]Zijun Zhang, Linqi Zhou, Liangke Gou, Ying Nian Wu:
Neural Architecture Search for Joint Optimization of Predictive Power and Biological Knowledge. CoRR abs/1909.00337 (2019) - [i36]Xianglei Xing, Tianfu Wu, Song-Chun Zhu, Ying Nian Wu:
Towards Interpretable Image Synthesis by Learning Sparsely Connected AND-OR Networks. CoRR abs/1909.04324 (2019) - [i35]Jianwen Xie, Song-Chun Zhu, Ying Nian Wu:
Learning Energy-based Spatial-Temporal Generative ConvNets for Dynamic Patterns. CoRR abs/1909.11975 (2019) - [i34]Dandan Zhu, Tian Han, Linqi Zhou, Xiaokang Yang, Ying Nian Wu:
Deep Unsupervised Clustering with Clustered Generator Model. CoRR abs/1911.08459 (2019) - [i33]Jianwen Xie, Ruiqi Gao, Zilong Zheng, Song-Chun Zhu, Ying Nian Wu:
Motion-Based Generator Model: Unsupervised Disentanglement of Appearance, Trackable and Intrackable Motions in Dynamic Patterns. CoRR abs/1911.11294 (2019) - [i32]Jianwen Xie, Ruiqi Gao, Erik Nijkamp, Song-Chun Zhu, Ying Nian Wu:
Representation Learning: A Statistical Perspective. CoRR abs/1911.11374 (2019) - [i31]Ruiqi Gao, Erik Nijkamp, Diederik P. Kingma, Zhen Xu, Andrew M. Dai, Ying Nian Wu:
Flow Contrastive Estimation of Energy-Based Models. CoRR abs/1912.00589 (2019) - [i30]Erik Nijkamp, Bo Pang, Tian Han, Linqi Zhou, Song-Chun Zhu, Ying Nian Wu:
Learning Deep Generative Models with Short Run Inference Dynamics. CoRR abs/1912.01909 (2019) - 2018
- [j22]Quanshi Zhang, Ying Nian Wu, Hao Zhang, Song-Chun Zhu:
Mining deep And-Or object structures via cost-sensitive question-answer-based active annotations. Comput. Vis. Image Underst. 176-177: 33-44 (2018) - [j21]Xingda Li, Yujing Guan, Yingnian Wu, Zhongbo Zhang:
Piano multipitch estimation using sparse coding embedded deep learning. EURASIP J. Audio Speech Music. Process. 2018: 11 (2018) - [j20]Jinxi Guo, Ning Xu, Kailun Qian, Yang Shi, Kaiyuan Xu, Yingnian Wu, Abeer Alwan:
Deep neural network based i-vector mapping for speaker verification using short utterances. Speech Commun. 105: 92-102 (2018) - [c37]Jianwen Xie, Yang Lu, Ruiqi Gao, Ying Nian Wu:
Cooperative Learning of Energy-Based Model and Latent Variable Model via MCMC Teaching. AAAI 2018: 4292-4301 - [c36]Quanshi Zhang, Ruiming Cao, Feng Shi, Ying Nian Wu, Song-Chun Zhu:
Interpreting CNN Knowledge via an Explanatory Graph. AAAI 2018: 4454-4463 - [c35]Jianwen Xie, Zilong Zheng, Ruiqi Gao, Wenguan Wang, Song-Chun Zhu, Ying Nian Wu:
Learning Descriptor Networks for 3D Shape Synthesis and Analysis. CVPR 2018: 8629-8638 - [c34]Quanshi Zhang, Ying Nian Wu, Song-Chun Zhu:
Interpretable Convolutional Neural Networks. CVPR 2018: 8827-8836 - [c33]Ruiqi Gao, Yang Lu, Junpei Zhou, Song-Chun Zhu, Ying Nian Wu:
Learning Generative ConvNets via Multi-Grid Modeling and Sampling. CVPR 2018: 9155-9164 - [c32]Tian Han, Xianglei Xing, Ying Nian Wu:
Learning Multi-view Generator Network for Shared Representation. ICPR 2018: 2062-2068 - [c31]Tian Han, Jiawen Wu, Ying Nian Wu:
Replicating Active Appearance Model by Generator Network. IJCAI 2018: 2205-2211 - [c30]Siyuan Huang, Siyuan Qi, Yinxue Xiao, Yixin Zhu, Ying Nian Wu, Song-Chun Zhu:
Cooperative Holistic Scene Understanding: Unifying 3D Object, Layout, and Camera Pose Estimation. NeurIPS 2018: 206-217 - [i29]Quanshi Zhang, Yu Yang, Ying Nian Wu, Song-Chun Zhu:
Interpreting CNNs via Decision Trees. CoRR abs/1802.00121 (2018) - [i28]Jianwen Xie, Zilong Zheng, Ruiqi Gao, Wenguan Wang, Song-Chun Zhu, Ying Nian Wu:
Learning Descriptor Networks for 3D Shape Synthesis and Analysis. CoRR abs/1804.00586 (2018) - [i27]Quanshi Zhang, Yu Yang, Ying Nian Wu, Song-Chun Zhu:
Network Transplanting. CoRR abs/1804.10272 (2018) - [i26]Quanshi Zhang, Yu Yang, Yuchen Liu, Ying Nian Wu, Song-Chun Zhu:
Unsupervised Learning of Neural Networks to Explain Neural Networks. CoRR abs/1805.07468 (2018) - [i25]Tian Han, Jiawen Wu, Ying Nian Wu:
Replicating Active Appearance Model by Generator Network. CoRR abs/1805.08704 (2018) - [i24]Xianglei Xing, Ruiqi Gao, Tian Han, Song-Chun Zhu, Ying Nian Wu:
Deformable Generator Network: Unsupervised Disentanglement of Appearance and Geometry. CoRR abs/1806.06298 (2018) - [i23]Tianmin Shu, Caiming Xiong, Ying Nian Wu, Song-Chun Zhu:
Interactive Agent Modeling by Learning to Probe. CoRR abs/1810.00510 (2018) - [i22]Ying Nian Wu, Ruiqi Gao, Tian Han, Song-Chun Zhu:
A Tale of Three Probabilistic Families: Discriminative, Descriptive and Generative Models. CoRR abs/1810.04261 (2018) - [i21]Ruiqi Gao, Jianwen Xie, Song-Chun Zhu, Ying Nian Wu:
Learning Grid-like Units with Vector Representation of Self-Position and Matrix Representation of Self-Motion. CoRR abs/1810.05597 (2018) - [i20]Jinxi Guo, Ning Xu, Kailun Qian, Yang Shi, Kaiyuan Xu, Yingnian Wu, Abeer Alwan:
Deep neural network based i-vector mapping for speaker verification using short utterances. CoRR abs/1810.07309 (2018) - [i19]Siyuan Huang, Siyuan Qi, Yinxue Xiao, Yixin Zhu, Ying Nian Wu, Song-Chun Zhu:
Cooperative Holistic Scene Understanding: Unifying 3D Object, Layout, and Camera Pose Estimation. CoRR abs/1810.13049 (2018) - [i18]Quanshi Zhang, Ruiming Cao, Ying Nian Wu, Song-Chun Zhu:
Mining Interpretable AOG Representations from Convolutional Networks via Active Question Answering. CoRR abs/1812.07996 (2018) - [i17]Quanshi Zhang, Xin Wang, Ruiming Cao, Ying Nian Wu, Feng Shi, Song-Chun Zhu:
Explanatory Graphs for CNNs. CoRR abs/1812.07997 (2018) - [i16]Jianwen Xie, Ruiqi Gao, Zilong Zheng, Song-Chun Zhu, Ying Nian Wu:
Learning Dynamic Generator Model by Alternating Back-Propagation Through Time. CoRR abs/1812.10587 (2018) - [i15]Tian Han, Erik Nijkamp, Xiaolin Fang, Mitch Hill, Song-Chun Zhu, Ying Nian Wu:
Divergence Triangle for Joint Training of Generator Model, Energy-based Model, and Inference Model. CoRR abs/1812.10907 (2018) - 2017
- [c29]Tian Han, Yang Lu, Song-Chun Zhu, Ying Nian Wu:
Alternating Back-Propagation for Generator Network. AAAI 2017: 1976-1984 - [c28]Quanshi Zhang, Ruiming Cao, Ying Nian Wu, Song-Chun Zhu:
Growing Interpretable Part Graphs on ConvNets via Multi-Shot Learning. AAAI 2017: 2898-2906 - [c27]Jianwen Xie, Song-Chun Zhu, Ying Nian Wu:
Synthesizing Dynamic Patterns by Spatial-Temporal Generative ConvNet. CVPR 2017: 1061-1069 - [c26]Jianwen Xie, Yifei Xu, Erik Nijkamp, Ying Nian Wu, Song-Chun Zhu:
Generative Hierarchical Learning of Sparse FRAME Models. CVPR 2017: 1933-1941 - [c25]Quanshi Zhang, Ruiming Cao, Ying Nian Wu, Song-Chun Zhu:
Mining Object Parts from CNNs via Active Question-Answering. CVPR 2017: 3890-3899 - [i14]Quanshi Zhang, Ruiming Cao, Ying Nian Wu, Song-Chun Zhu:
Mining Object Parts from CNNs via Active Question-Answering. CoRR abs/1704.03173 (2017) - [i13]Quanshi Zhang, Ruiming Cao, Shengming Zhang, Mark Edmonds, Ying Nian Wu, Song-Chun Zhu:
Interactively Transferring CNN Patterns for Part Localization. CoRR abs/1708.01783 (2017) - [i12]Quanshi Zhang, Ruiming Cao, Feng Shi, Ying Nian Wu, Song-Chun Zhu:
Interpreting CNN knowledge via an Explanatory Graph. CoRR abs/1708.01785 (2017) - [i11]Quanshi Zhang, Ying Nian Wu, Song-Chun Zhu:
A Cost-Sensitive Visual Question-Answer Framework for Mining a Deep And-OR Object Semantics from Web Images. CoRR abs/1708.03911 (2017) - [i10]Ruiqi Gao, Yang Lu, Junpei Zhou, Song-Chun Zhu, Ying Nian Wu:
Learning Multi-grid Generative ConvNets by Minimal Contrastive Divergence. CoRR abs/1709.08868 (2017) - [i9]Quanshi Zhang, Ying Nian Wu, Song-Chun Zhu:
Interpretable Convolutional Neural Networks. CoRR abs/1710.00935 (2017) - 2016
- [j19]Kuo-Jung Lee, Ray-Bing Chen, Ying Nian Wu:
Bayesian variable selection for finite mixture model of linear regressions. Comput. Stat. Data Anal. 95: 1-16 (2016) - [j18]Yongliang Luo, Yingnian Wu, Yuanhui Qin, Lin Zhang, Yuanming Wang:
Modeling method for integration of air command and security process. Int. J. Model. Simul. Sci. Comput. 7(1): 1641004:1-1641004:17 (2016) - [c24]Yang Lu, Song-Chun Zhu, Ying Nian Wu:
Learning FRAME Models Using CNN Filters. AAAI 2016: 1902-1910 - [c23]Jianwen Xie, Yang Lu, Song-Chun Zhu, Ying Nian Wu:
A Theory of Generative ConvNet. ICML 2016: 2635-2644 - [i8]Jianwen Xie, Yang Lu, Song-Chun Zhu, Ying Nian Wu:
A Theory of Generative ConvNet. CoRR abs/1602.03264 (2016) - [i7]Jianwen Xie, Song-Chun Zhu, Ying Nian Wu:
Synthesizing Dynamic Textures and Sounds by Spatial-Temporal Generative ConvNet. CoRR abs/1606.00972 (2016) - [i6]Tian Han, Yang Lu, Song-Chun Zhu, Ying Nian Wu:
Learning Generative ConvNet with Continuous Latent Factors by Alternating Back-Propagation. CoRR abs/1606.08571 (2016) - [i5]Jianwen Xie, Pamela K. Douglas, Ying Nian Wu, Arthur L. Brody, Ariana E. Anderson:
Decoding the Encoding of Functional Brain Networks: an fMRI Classification Comparison of Non-negative Matrix Factorization (NMF), Independent Component Analysis (ICA), and Sparse Coding Algorithms. CoRR abs/1607.00435 (2016) - [i4]Jianwen Xie, Yang Lu, Song-Chun Zhu, Ying Nian Wu:
Cooperative Training of Descriptor and Generator Networks. CoRR abs/1609.09408 (2016) - [i3]Quanshi Zhang, Ruiming Cao, Ying Nian Wu, Song-Chun Zhu:
Multi-Shot Mining Semantic Part Concepts in CNNs. CoRR abs/1611.04246 (2016) - 2015
- [j17]Jianwen Xie, Wenze Hu, Song-Chun Zhu, Ying Nian Wu:
Learning Sparse FRAME Models for Natural Image Patterns. Int. J. Comput. Vis. 114(2-3): 91-112 (2015) - [c22]Quanshi Zhang, Ying Nian Wu, Song-Chun Zhu:
Mining And-Or Graphs for Graph Matching and Object Discovery. ICCV 2015: 55-63 - [c21]Jifeng Dai, Ying Nian Wu:
Generative Modeling of Convolutional Neural Networks. ICLR (Poster) 2015 - [i2]Yang Lu, Song-Chun Zhu, Ying Nian Wu:
Learning FRAME Models Using CNN Filters for Knowledge Visualization. CoRR abs/1509.08379 (2015) - 2014
- [j16]Yingnian Wu, Guojun Yang, Lin Zhang:
Mouse simulation in human-machine interface using kinect and 3 gear systems. Int. J. Model. Simul. Sci. Comput. 5(4): 1450015 (2014) - [j15]Ariana E. Anderson, Pamela K. Douglas, Wesley T. Kerr, Virginia S. Haynes, Alan L. Yuille, Jianwen Xie, Ying Nian Wu, Jesse A. Brown, Mark S. Cohen:
Non-negative matrix factorization of multimodal MRI, fMRI and phenotypic data reveals differential changes in default mode subnetworks in ADHD. NeuroImage 102: 207-219 (2014) - [c20]Jianwen Xie, Wenze Hu, Song-Chun Zhu, Ying Nian Wu:
Learning Inhomogeneous FRAME Models for Object Patterns. CVPR 2014: 1035-1042 - [c19]Jifeng Dai, Yi Hong, Wenze Hu, Song-Chun Zhu, Ying Nian Wu:
Unsupervised Learning of Dictionaries of Hierarchical Compositional Models. CVPR 2014: 2505-2512 - [c18]Wan-Ping Chen, Ying Nian Wu, Ray-Bing Chen:
Bayesian Variable Selection for Multi-response Linear Regression. TAAI 2014: 74-88 - [r4]Ying Nian Wu:
Cross Entropy. Computer Vision, A Reference Guide 2014: 154 - [r3]Ying Nian Wu:
Data Augmentation. Computer Vision, A Reference Guide 2014: 165-166 - [r2]Ying Nian Wu:
Histogram. Computer Vision, A Reference Guide 2014: 361-362 - [r1]Ying Nian Wu:
Statistical Independence. Computer Vision, A Reference Guide 2014: 759-760 - [i1]Zhuowen Tu, Piotr Dollár, Yingnian Wu:
Layered Logic Classifiers: Exploring the 'And' and 'Or' Relations. CoRR abs/1405.6804 (2014) - 2013
- [c17]Jifeng Dai, Ying Nian Wu, Jie Zhou, Song-Chun Zhu:
Cosegmentation and Cosketch by Unsupervised Learning. ICCV 2013: 1305-1312 - 2012
- [j14]Yuewei Shen, Lin Zhang, Dengkun Liu, Yingnian Wu, Lan Mu, Ralph C. Huntsinger:
Comparisons of Ray-Tracing and parabolic equation Methods for the Large-Scale Complex electromagnetic Environment simulations. Int. J. Model. Simul. Sci. Comput. 3(2): 1240005 (2012) - [c16]Yuewei Shen, Lin Zhang, Yingnian Wu, Lan Mu, Yandong Lv:
Methods to Improve Accuracy and Speed for the Quasi-3D Electromagnetic Environment Simulation. AsiaSim (3) 2012: 53-59 - 2011
- [j13]Ray-Bing Chen, Chi-Hsiang Chu, Te-You Lai, Ying Nian Wu:
Stochastic matching pursuit for Bayesian variable selection. Stat. Comput. 21(2): 247-259 (2011) - [c15]Wenze Hu, Ying Nian Wu, Song-Chun Zhu:
Image representation by active curves. ICCV 2011: 1808-1815 - 2010
- [j12]Ying Nian Wu, Zhangzhang Si, Haifeng Gong, Song Chun Zhu:
Learning Active Basis Model for Object Detection and Recognition. Int. J. Comput. Vis. 90(2): 198-235 (2010) - [c14]Zhangzhang Si, Ying Nian Wu:
Wavelet, active basis, and shape script: a tour in the sparse land. Multimedia Information Retrieval 2010: 201-210
2000 – 2009
- 2009
- [c13]Zhangzhang Si, Haifeng Gong, Ying Nian Wu, Song Chun Zhu:
Learning mixed templates for object recognition. CVPR 2009: 272-279 - 2007
- [j11]Ray-Bing Chen, Ying Nian Wu:
A null space method for over-complete blind source separation. Comput. Stat. Data Anal. 51(12): 5519-5536 (2007) - [j10]Cheng-en Guo, Song Chun Zhu, Ying Nian Wu:
Primal sketch: Integrating structure and texture. Comput. Vis. Image Underst. 106(1): 5-19 (2007) - [j9]Ying Nian Wu, Jinhui Li, Ziqiang Liu, Song-Chun Zhu:
Statistical Principles in Image Modeling. Technometrics 49(3): 249-261 (2007) - [c12]Ying Nian Wu, Zhangzhang Si, Chuck Fleming, Song Chun Zhu:
Deformable Template As Active Basis. ICCV 2007: 1-8 - 2004
- [c11]Cheng-en Guo, Ying Nian Wu, Song Chun Zhu:
Information Scaling Laws in Natural Scenes. CVPR Workshops 2004: 193 - 2003
- [j8]Gianfranco Doretto, Alessandro Chiuso, Ying Nian Wu, Stefano Soatto:
Dynamic Textures. Int. J. Comput. Vis. 51(2): 91-109 (2003) - [j7]Cheng-en Guo, Song Chun Zhu, Ying Nian Wu:
Modeling Visual Patterns by Integrating Descriptive and Generative Methods. Int. J. Comput. Vis. 53(1): 5-29 (2003) - [c10]Cheng-en Guo, Song Chun Zhu, Ying Nian Wu:
Towards a Mathematical Theory of Primal Sketch and Sketchability. ICCV 2003: 1228-1235 - 2002
- [c9]Ying Nian Wu, Song Chun Zhu, Cheng-en Guo:
Statistical Modeling of Texture Sketch. ECCV (3) 2002: 240-254 - [c8]Song Chun Zhu, Cheng-en Guo, Ying Nian Wu, Yizhou Wang:
What Are Textons? ECCV (4) 2002: 793-807 - 2001
- [j6]Alan L. Yuille, James M. Coughlan, Ying Nian Wu, Song Chun Zhu:
Order Parameters for Detecting Target Curves in Images: When Does High Level Knowledge Help? Int. J. Comput. Vis. 41(1/2): 9-33 (2001) - [c7]Payam Saisan, Gianfranco Doretto, Ying Nian Wu, Stefano Soatto:
Dynamic Texture Recognition. CVPR (2) 2001: 58-63 - [c6]Cheng-en Guo, Song Chun Zhu, Ying Nian Wu:
Visual Learning by Integrating Descriptive and Generative Methods. ICCV 2001: 370-377 - [c5]Stefano Soatto, Gianfranco Doretto, Ying Nian Wu:
Dynamic Textures. ICCV 2001: 439-446 - 2000
- [j5]Ying Nian Wu, Song Chun Zhu, Xiuwen Liu:
Equivalence of Julesz Ensembles and FRAME Models. Int. J. Comput. Vis. 38(3): 247-265 (2000) - [j4]Song Chun Zhu, Xiuwen Liu, Ying Nian Wu:
Exploring Texture Ensembles by Efficient Markov Chain Monte Carlo-Toward a 'Trichromacy' Theory of Texture. IEEE Trans. Pattern Anal. Mach. Intell. 22(6): 554-569 (2000) - [c4]Alan L. Yuille, James M. Coughlan, Song Chun Zhu, Ying Nian Wu:
Order Parameters for Minimax Entropy Distributions: When Does High Level Knowledge Help? CVPR 2000: 1558-1565
1990 – 1999
- 1999
- [j3]Song Chun Zhu, Ying Nian Wu:
From local features to global perception - A perspective of Gestalt psychology from Markov random field theory. Neurocomputing 26-27: 939-945 (1999) - [c3]Ying Nian Wu, Song Chun Zhu, Xiuwen Liu:
Equivalence of Julesz and Gibbs Texture Ensembles. ICCV 1999: 1025-1032 - 1998
- [j2]Song Chun Zhu, Ying Nian Wu, David Mumford:
Filters, Random Fields and Maximum Entropy (FRAME): Towards a Unified Theory for Texture Modeling. Int. J. Comput. Vis. 27(2): 107-126 (1998) - 1997
- [j1]Song Chun Zhu, Ying Nian Wu, David Mumford:
Minimax Entropy Principle and Its Application to Texture Modeling. Neural Comput. 9(8): 1627-1660 (1997) - [c2]Song-Chun Zhu, Ying Nian Wu, David Mumford:
Modeling images and textures by minimax entropy. Human Vision and Electronic Imaging 1997: 387-401 - 1996
- [c1]Song Chun Zhu, Ying Nian Wu, David Mumford:
FRAME: Filters, Random fields, and Minimax Entropy - Towards a Unified Theory for Texture Modeling. CVPR 1996: 686-693
Coauthor Index
Song-Chun Zhu
aka: Song Chun Zhu
aka: Song Chun Zhu
[c97] [i114] [i105] [c91] [c89] [c88] [c87] [c85] [c84] [i99] [i92] [i89] [i83] [j34] [j33] [j32] [j31] [c80] [c76] [c75] [c74] [i80] [i79] [i78] [i75] [j30] [j29] [j28] [j27] [c71] [c70] [c67] [c65] [c60] [c57] [c56] [c55] [i74] [i73] [i72] [i71] [i70] [i68] [i61] [i60] [i59] [j26] [c54] [c53] [c51] [c50] [c49] [c48] [c46] [i58] [i57] [i56] [i55] [i54] [i53] [i52] [i48] [j23] [c45] [c43] [c42] [c40] [c39] [i47] [i44] [i43] [i42] [i41] [i38] [i36] [i35] [i33] [i32] [i30] [j22] [c36] [c35] [c34] [c33] [c30] [i29] [i28] [i27] [i26] [i24] [i23] [i22] [i21] [i19] [i18] [i17] [i16] [i15] [c29] [c28] [c27] [c26] [c25] [i14] [i13] [i12] [i11] [i10] [i9] [c24] [c23] [i8] [i7] [i6] [i4] [i3] [j17] [c22] [i2] [c20] [c19] [c17] [c15] [j12] [c13] [j10] [j9] [c12] [c11] [j7] [c10] [c9] [c8] [j6] [c6] [j5] [j4] [c4] [j3] [c3] [j2] [j1] [c2] [c1]
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