Wu, Ying Nian

108 publications

ICML 2025 An Expressive and Self-Adaptive Dynamical System for Efficient Function Learning Chuan Liu, Chunshu Wu, Ruibing Song, Ang Li, Ying Nian Wu, Tong Geng

ICLR 2025 DS-LLM: Leveraging Dynamical Systems to Enhance Both Training and Inference of Large Language Models Ruibing Song, Chuan Liu, Chunshu Wu, Ang Li, Dongfang Liu, Ying Nian Wu, Tong Geng

ICLR 2025 Diff-PIC: Revolutionizing Particle-in-Cell Nuclear Fusion Simulation with Diffusion Models Chuan Liu, Chunshu Wu, Shihui Cao, Mingkai Chen, James Chenhao Liang, Ang Li, Michael Huang, Chuang Ren, Ying Nian Wu, Dongfang Liu, Tong Geng

NeurIPS 2025 Embodied Web Agents: Bridging Physical-Digital Realms for Integrated Agent Intelligence Yining Hong, Rui Sun, Bingxuan Li, Xingcheng Yao, Maxine Wu, Alexander Chien, Da Yin, Ying Nian Wu, Zhecan Wang, Kai-Wei Chang

CoRL 2025 Latent Adaptive Planner for Dynamic Manipulation Donghun Noh, Deqian Kong, Minglu Zhao, Andrew Lizarraga, Jianwen Xie, Ying Nian Wu, Dennis Hong

TMLR 2025 Latent Space Energy-Based Neural ODEs Sheng Cheng, Deqian Kong, Jianwen Xie, Kookjin Lee, Ying Nian Wu, Yezhou Yang

ICML 2025 Latent Thought Models with Variational Bayes Inference-Time Computation Deqian Kong, Minglu Zhao, Dehong Xu, Bo Pang, Shu Wang, Edouardo Honig, Zhangzhang Si, Chuan Li, Jianwen Xie, Sirui Xie, Ying Nian Wu

ICLRW 2025 Long-Range Gene Expression Prediction with Token Alignment of Large Language Model Edouardo Honig, Huixin Zhan, Ying Nian Wu, Zijun Frank Zhang

NeurIPS 2025 MLZero: A Multi-Agent System for End-to-End Machine Learning Automation Haoyang Fang, Boran Han, Nick Erickson, Xiyuan Zhang, Su Zhou, Anirudh Dagar, Jiani Zhang, Ali Caner Turkmen, Cuixiong Hu, Huzefa Rangwala, Ying Nian Wu, Bernie Wang, George Karypis

AAAI 2025 Monitoring Primitive Interactions During the Training of DNNs Jie Ren, Xinhao Zheng, Jiyu Liu, Andrew Lizarraga, Ying Nian Wu, Liang Lin, Quanshi Zhang

LoG 2025 NP-NDS: A Nature-Powered Nonlinear Dynamical System for Power Grid Forecasting Chunshu Wu, Ruibing Song, Chuan Liu, Yuqing Wang, Yousu Chen, Ang Li, Dongfang Liu, Ying Nian Wu, Michael Huang, Tong Geng

ICLR 2025 On Conformal Isometry of Grid Cells: Learning Distance-Preserving Position Embedding Dehong Xu, Ruiqi Gao, Wenhao Zhang, Xue-Xin Wei, Ying Nian Wu

NeurIPS 2025 Optimizing the Unknown: Black Box Bayesian Optimization with Energy-Based Model and Reinforcement Learning Ruiyao Miao, Junren Xiao, Shiya Tsang, Hui Xiong, Ying Nian Wu

NeurIPS 2025 Place Cells as Multi-Scale Position Embeddings: Random Walk Transition Kernels for Path Planning Minglu Zhao, Dehong Xu, Deqian Kong, Wenhao Zhang, Ying Nian Wu

ICLR 2025 SlowFast-VGen: Slow-Fast Learning for Action-Driven Long Video Generation Yining Hong, Beide Liu, Maxine Wu, Yuanhao Zhai, Kai-Wei Chang, Linjie Li, Kevin Lin, Chung-Ching Lin, Jianfeng Wang, Zhengyuan Yang, Ying Nian Wu, Lijuan Wang

NeurIPS 2025 SwS: Self-Aware Weakness-Driven Problem Synthesis in Reinforcement Learning for LLM Reasoning Xiao Liang, Zhong-Zhi Li, Yeyun Gong, Yang Wang, Hengyuan Zhang, Yelong Shen, Ying Nian Wu, Weizhu Chen

ICLR 2025 Visual Agents as Fast and Slow Thinkers Guangyan Sun, Mingyu Jin, Zhenting Wang, Cheng-Long Wang, Siqi Ma, Qifan Wang, Tong Geng, Ying Nian Wu, Yongfeng Zhang, Dongfang Liu

NeurIPSW 2024 A Minimalistic Representation Model for Head Direction System Minglu Zhao, Dehong Xu, Deqian Kong, Wenhao Zhang, Ying Nian Wu

NeurIPS 2024 Atlas3D: Physically Constrained Self-Supporting Text-to-3D for Simulation and Fabrication Yunuo Chen, Tianyi Xie, Zeshun Zong, Xuan Li, Feng Gao, Yin Yang, Ying Nian Wu, Chenfanfu Jiang

NeurIPSW 2024 Better Prompt Compression Without Multi-Layer Perceptrons Edouardo Honig, Andrew Lizarraga, Zijun Frank Zhang, Ying Nian Wu

NeurIPS 2024 EM Distillation for One-Step Diffusion Models Sirui Xie, Zhisheng Xiao, Diederik P. Kingma, Tingbo Hou, Ying Nian Wu, Kevin Murphy, Tim Salimans, Ben Poole, Ruiqi Gao

NeurIPS 2024 Flow Priors for Linear Inverse Problems via Iterative Corrupted Trajectory Matching Yasi Zhang, Peiyu Yu, Yaxuan Zhu, Yingshan Chang, Feng Gao, Ying Nian Wu, Oscar Leong

ICLR 2024 Image Translation as Diffusion Visual Programmers Cheng Han, James Chenhao Liang, Qifan Wang, Majid Rabbani, Sohail Dianat, Raghuveer Rao, Ying Nian Wu, Dongfang Liu

ICMLW 2024 LLM3: Large Language Model-Based Task and Motion Planning with Motion Failure Reasoning Shu Wang, Muzhi Han, Ziyuan Jiao, Zeyu Zhang, Ying Nian Wu, Song-Chun Zhu, Hangxin Liu

NeurIPS 2024 Latent Plan Transformer for Trajectory Abstraction: Planning as Latent Space Inference Deqian Kong, Dehong Xu, Minglu Zhao, Bo Pang, Jianwen Xie, Andrew Lizarraga, Yuhao Huang, Sirui Xie, Ying Nian Wu

ICLR 2024 Learning Energy-Based Models by Cooperative Diffusion Recovery Likelihood Yaxuan Zhu, Jianwen Xie, Ying Nian Wu, Ruiqi Gao

CVPR 2024 Learning for Transductive Threshold Calibration in Open-World Recognition Qin Zhang, Dongsheng An, Tianjun Xiao, Tong He, Qingming Tang, Ying Nian Wu, Joseph Tighe, Yifan Xing

NeurIPS 2024 Molecule Design by Latent Prompt Transformer Deqian Kong, Yuhao Huang, Jianwen Xie, Edouardo Honig, Ming Xu, Shuanghong Xue, Pei Lin, Sanping Zhou, Sheng Zhong, Nanning Zheng, Ying Nian Wu

ICLR 2024 Neural-Symbolic Recursive Machine for Systematic Generalization Qing Li, Yixin Zhu, Yitao Liang, Ying Nian Wu, Song-Chun Zhu, Siyuan Huang

AAAI 2024 No Head Left Behind - Multi-Head Alignment Distillation for Transformers Tianyang Zhao, Kunwar Yashraj Singh, Srikar Appalaraju, Peng Tang, Vijay Mahadevan, R. Manmatha, Ying Nian Wu

ECCV 2024 Object-Conditioned Energy-Based Attention mAP Alignment in Text-to-Image Diffusion Models Yasi Zhang, Peiyu Yu, Ying Nian Wu

ECCV 2024 Skews in the Phenomenon Space Hinder Generalization in Text-to-Image Generation Yingshan Chang, Yasi Zhang, Zhiyuan Fang, Ying Nian Wu, Yonatan Bisk, Feng Gao

NeurIPS 2024 The Motion Planning Neural Circuit in Goal-Directed Navigation as Lie Group Operator Search Junfeng Zuo, Ying Nian Wu, Si Wu, Wen-Hao Zhang

ICLR 2024 Threshold-Consistent Margin Loss for Open-World Deep Metric Learning Qin Zhang, Linghan Xu, Jun Fang, Qingming Tang, Ying Nian Wu, Joseph Tighe, Yifan Xing

NeurIPS 2024 Visual Fourier Prompt Tuning Runjia Zeng, Cheng Han, Qifan Wang, Chunshu Wu, Tong Geng, Lifu Huang, Ying Nian Wu, Dongfang Liu

ICLR 2023 A Minimalist Dataset for Systematic Generalization of Perception, Syntax, and Semantics Qing Li, Siyuan Huang, Yining Hong, Yixin Zhu, Ying Nian Wu, Song-Chun Zhu

NeurIPS 2023 A Recurrent Neural Circuit Mechanism of Temporal-Scaling Equivariant Representation Junfeng Zuo, Xiao Liu, Ying Nian Wu, Si Wu, Wenhao Zhang

NeurIPSW 2023 Automated Distillation of Genomic Equations Governing Single Cell Gene Expression Edouardo Honig, Frederique Ruf-Zamojski, Stuart Sealfon, Ying Nian Wu, Zijun Frank Zhang

NeurIPS 2023 Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models Pan Lu, Baolin Peng, Hao Cheng, Michel Galley, Kai-Wei Chang, Ying Nian Wu, Song-Chun Zhu, Jianfeng Gao

NeurIPSW 2023 Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models Pan Lu, Baolin Peng, Hao Cheng, Michel Galley, Kai-Wei Chang, Ying Nian Wu, Song-Chun Zhu, Jianfeng Gao

ICML 2023 Diverse and Faithful Knowledge-Grounded Dialogue Generation via Sequential Posterior Inference Yan Xu, Deqian Kong, Dehong Xu, Ziwei Ji, Bo Pang, Pascale Fung, Ying Nian Wu

ICLR 2023 Dynamic Prompt Learning via Policy Gradient for Semi-Structured Mathematical Reasoning Pan Lu, Liang Qiu, Kai-Wei Chang, Ying Nian Wu, Song-Chun Zhu, Tanmay Rajpurohit, Peter Clark, Ashwin Kalyan

NeurIPS 2023 Learning Energy-Based Prior Model with Diffusion-Amortized MCMC Peiyu Yu, Yaxuan Zhu, Sirui Xie, Xiaojian Ma, Ruiqi Gao, Song-Chun Zhu, Ying Nian Wu

ICCV 2023 Learning Hierarchical Features with Joint Latent Space Energy-Based Prior Jiali Cui, Ying Nian Wu, Tian Han

CVPR 2023 Learning Joint Latent Space EBM Prior Model for Multi-Layer Generator Jiali Cui, Ying Nian Wu, Tian Han

NeurIPSW 2023 Molecule Design by Latent Prompt Transformer Deqian Kong, Yuhao Huang, Jianwen Xie, Ying Nian Wu

UAI 2023 Molecule Design by Latent Space Energy-Based Modeling and Gradual Distribution Shifting Deqian Kong, Bo Pang, Tian Han, Ying Nian Wu

ICML 2023 On the Complexity of Bayesian Generalization Yu-Zhe Shi, Manjie Xu, John E. Hopcroft, Kun He, Joshua B. Tenenbaum, Song-Chun Zhu, Ying Nian Wu, Wenjuan Han, Yixin Zhu

NeurIPSW 2022 Conformal Isometry of Lie Group Representation in Recurrent Network of Grid Cells Dehong Xu, Ruiqi Gao, Wenhao Zhang, Xue-Xin Wei, Ying Nian Wu

ICML 2022 Latent Diffusion Energy-Based Model for Interpretable Text Modelling Peiyu Yu, Sirui Xie, Xiaojian Ma, Baoxiong Jia, Bo Pang, Ruiqi Gao, Yixin Zhu, Song-Chun Zhu, Ying Nian Wu

ECCV 2022 Learning Algebraic Representation for Systematic Generalization in Abstract Reasoning Chi Zhang, Sirui Xie, Baoxiong Jia, Ying Nian Wu, Song-Chun Zhu, Yixin Zhu

AAAI 2022 Learning V1 Simple Cells with Vector Representation of Local Content and Matrix Representation of Local Motion Ruiqi Gao, Jianwen Xie, Siyuan Huang, Yufan Ren, Song-Chun Zhu, Ying Nian Wu

ICLR 2022 MCMC Should Mix: Learning Energy-Based Model with Neural Transport Latent Space MCMC Erik Nijkamp, Ruiqi Gao, Pavel Sountsov, Srinivas Vasudevan, Bo Pang, Song-Chun Zhu, Ying Nian Wu

AAAI 2022 SAS: Self-Augmentation Strategy for Language Model Pre-Training Yifei Xu, Jingqiao Zhang, Ru He, Liangzhu Ge, Chao Yang, Cheng Yang, Ying Nian Wu

CVPR 2022 Transform-Retrieve-Generate: Natural Language-Centric Outside-Knowledge Visual Question Answering Feng Gao, Qing Ping, Govind Thattai, Aishwarya Reganti, Ying Nian Wu, Prem Natarajan

NeurIPS 2022 Translation-Equivariant Representation in Recurrent Networks with a Continuous Manifold of Attractors Wenhao Zhang, Ying Nian Wu, Si Wu

NeurIPSW 2021 Deep Generative Model with Hierarchical Latent Factors for Timeseries Anomaly Detection Cristian Ignacio Challu, Peihong Jiang, Ying Nian Wu, Laurent Callot

CVPR 2021 Generative PointNet: Deep Energy-Based Learning on Unordered Point Sets for 3D Generation, Reconstruction and Classification Jianwen Xie, Yifei Xu, Zilong Zheng, Song-Chun Zhu, Ying Nian Wu

NeurIPS 2021 Iterative Teacher-Aware Learning Luyao Yuan, Dongruo Zhou, Junhong Shen, Jingdong Gao, Jeffrey L Chen, Quanquan Gu, Ying Nian Wu, Song-Chun Zhu

ICML 2021 Latent Space Energy-Based Model of Symbol-Vector Coupling for Text Generation and Classification Bo Pang, Ying Nian Wu

AAAI 2021 Learning Cycle-Consistent Cooperative Networks via Alternating MCMC Teaching for Unsupervised Cross-Domain Translation Jianwen Xie, Zilong Zheng, Xiaolin Fang, Song-Chun Zhu, Ying Nian Wu

ICLR 2021 Learning Energy-Based Models by Diffusion Recovery Likelihood Ruiqi Gao, Yang Song, Ben Poole, Ying Nian Wu, Diederik P Kingma

CVPR 2021 Learning Neural Representation of Camera Pose with Matrix Representation of Pose Shift via View Synthesis Yaxuan Zhu, Ruiqi Gao, Siyuan Huang, Song-Chun Zhu, Ying Nian Wu

NeurIPS 2021 On Path Integration of Grid Cells: Group Representation and Isotropic Scaling Ruiqi Gao, Jianwen Xie, Xue-Xin Wei, Song-Chun Zhu, Ying Nian Wu

CVPR 2021 Trajectory Prediction with Latent Belief Energy-Based Model Bo Pang, Tianyang Zhao, Xu Xie, Ying Nian Wu

NeurIPS 2021 Unsupervised Foreground Extraction via Deep Region Competition Peiyu Yu, Sirui Xie, Xiaojian Ma, Yixin Zhu, Ying Nian Wu, Song-Chun Zhu

NeurIPSW 2021 Unsupervised Meta-Learning via Latent Space Energy-Based Model of Symbol Vector Coupling Deqian Kong, Bo Pang, Ying Nian Wu

ICML 2020 Closed Loop Neural-Symbolic Learning via Integrating Neural Perception, Grammar Parsing, and Symbolic Reasoning Qing Li, Siyuan Huang, Yining Hong, Yixin Chen, Ying Nian Wu, Song-Chun Zhu

NeurIPSW 2020 From EM-Projections to Variational Auto-Encoder Tian Han, Jun Zhang, Ying Nian Wu

NeurIPS 2020 Learning Latent Space Energy-Based Prior Model Bo Pang, Tian Han, Erik Nijkamp, Song-Chun Zhu, Ying Nian Wu

ECCV 2020 Learning Multi-Layer Latent Variable Model via Variational Optimization of Short Run MCMC for Approximate Inference Erik Nijkamp, Bo Pang, Tian Han, Linqi Zhou, Song-Chun Zhu, Ying Nian Wu

AAAI 2020 Motion-Based Generator Model: Unsupervised Disentanglement of Appearance, Trackable and Intrackable Motions in Dynamic Patterns Jianwen Xie, Ruiqi Gao, Zilong Zheng, Song-Chun Zhu, Ying Nian Wu

AAAI 2020 On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models Erik Nijkamp, Mitch Hill, Tian Han, Song-Chun Zhu, Ying Nian Wu

NeurIPSW 2020 Semi-Supervised Learning by Latent Space Energy-Based Model of Symbol-Vector Coupling Bo Pang, Erik Nijkamp, Jiali Cui, Tian Han, Ying Nian Wu

AAAI 2019 Learning Dynamic Generator Model by Alternating Back-Propagation Through Time Jianwen Xie, Ruiqi Gao, Zilong Zheng, Song-Chun Zhu, Ying Nian Wu

WACV 2019 Learning Generator Networks for Dynamic Patterns Tian Han, Yang Lu, Jiawen Wu, Xianglei Xing, Ying Nian Wu

ICLR 2019 Learning Grid Cells as Vector Representation of Self-Position Coupled with Matrix Representation of Self-Motion Ruiqi Gao, Jianwen Xie, Song-Chun Zhu, Ying Nian Wu

NeurIPS 2019 Learning Non-Convergent Non-Persistent Short-Run MCMC Toward Energy-Based Model Erik Nijkamp, Mitch Hill, Song-Chun Zhu, Ying Nian Wu

NeurIPS 2018 Cooperative Holistic Scene Understanding: Unifying 3D Object, Layout, and Camera Pose Estimation Siyuan Huang, Siyuan Qi, Yinxue Xiao, Yixin Zhu, Ying Nian Wu, Song-Chun Zhu

AAAI 2018 Cooperative Learning of Energy-Based Model and Latent Variable Model via MCMC Teaching Jianwen Xie, Yang Lu, Ruiqi Gao, Ying Nian Wu

AAAI 2018 Interpreting CNN Knowledge via an Explanatory Graph Quanshi Zhang, Ruiming Cao, Feng Shi, Ying Nian Wu, Song-Chun Zhu

IJCAI 2018 Replicating Active Appearance Model by Generator Network Tian Han, Jiawen Wu, Ying Nian Wu

AAAI 2017 Alternating Back-Propagation for Generator Network Tian Han, Yang Lu, Song-Chun Zhu, Ying Nian Wu

CVPR 2017 Generative Hierarchical Learning of Sparse FRAME Models Jianwen Xie, Yifei Xu, Erik Nijkamp, Ying Nian Wu, Song-Chun Zhu

AAAI 2017 Growing Interpretable Part Graphs on ConvNets via Multi-Shot Learning Quanshi Zhang, Ruiming Cao, Ying Nian Wu, Song-Chun Zhu

CVPR 2017 Mining Object Parts from CNNs via Active Question-Answering Quanshi Zhang, Ruiming Cao, Ying Nian Wu, Song-Chun Zhu

CVPR 2017 Synthesizing Dynamic Patterns by Spatial-Temporal Generative ConvNet Jianwen Xie, Song-Chun Zhu, Ying Nian Wu

AAAI 2016 Learning FRAME Models Using CNN Filters Yang Lu, Song-Chun Zhu, Ying Nian Wu

ICLR 2015 Generative Modeling of Convolutional Neural Networks Jifeng Dai, Ying Nian Wu

ICCV 2015 Mining And-or Graphs for Graph Matching and Object Discovery Quanshi Zhang, Ying Nian Wu, Song-Chun Zhu

CVPR 2014 Learning Inhomogeneous FRAME Models for Object Patterns Jianwen Xie, Wenze Hu, Song-Chun Zhu, Ying Nian Wu

CVPR 2014 Unsupervised Learning of Dictionaries of Hierarchical Compositional Models Jifeng Dai, Yi Hong, Wenze Hu, Song-Chun Zhu, Ying Nian Wu

ICCV 2013 Cosegmentation and Cosketch by Unsupervised Learning Jifeng Dai, Ying Nian Wu, Jie Zhou, Song-Chun Zhu

ICCV 2011 Image Representation by Active Curves Wenze Hu, Ying Nian Wu, Song-Chun Zhu

CVPR 2009 Learning Mixed Templates for Object Recognition Zhangzhang Si, Haifeng Gong, Ying Nian Wu, Song Chun Zhu

ICCV 2007 Deformable Template as Active Basis Ying Nian Wu, Zhangzhang Si, Chuck Fleming, Song Chun Zhu

CVPR 2004 Information Scaling Laws in Natural Scenes Cheng-en Guo, Ying Nian Wu, Song Chun Zhu

CVPRW 2004 Information Scaling Laws in Natural Scenes Cheng-en Guo, Ying Nian Wu, Song Chun Zhu

ICCV 2003 Towards a Mathematical Theory of Primal Sketch and Sketchability Cheng-en Guo, Song Chun Zhu, Ying Nian Wu

ECCV 2002 Statistical Modeling of Texture Sketch Ying Nian Wu, Song Chun Zhu, Cheng-en Guo

ECCV 2002 What Are Textons? Song Chun Zhu, Cheng-en Guo, Ying Nian Wu, Yizhou Wang

CVPR 2001 Dynamic Texture Recognition Payam Saisan, Gianfranco Doretto, Ying Nian Wu, Stefano Soatto

ICCV 2001 Dynamic Textures Stefano Soatto, Gianfranco Doretto, Ying Nian Wu

ICCV 2001 Visual Learning by Integrating Descriptive and Generative Methods Cheng-en Guo, Song Chun Zhu, Ying Nian Wu

CVPR 2000 Order Parameters for Minimax Entropy Distributions: When Does High Level Knowledge Help? Alan L. Yuille, James M. Coughlan, Song Chun Zhu, Ying Nian Wu

ICCV 1999 Equivalence of Julesz and Gibbs Texture Ensembles Ying Nian Wu, Song Chun Zhu, Xiuwen Liu

NeCo 1997 Minimax Entropy Principle and Its Application to Texture Modeling Song Chun Zhu, Ying Nian Wu, David Mumford

CVPR 1996 FRAME: Filters, Random Fields, and Minimax Entropy - Towards a Unified Theory for Texture Modeling Song Chun Zhu, Ying Nian Wu, David Mumford