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

Modeling images and textures by minimax entropy. - Song-Chun Zhu; Ying Nian Wu; David Mumford (Human Vision and Electronic Imaging, 1997)

Minimax Entropy Principle and Its Application to Texture Modeling. - Song Chun Zhu; Ying Nian Wu; David Mumford (Neural Comput., 1997)

Filters, Random Fields and Maximum Entropy (FRAME): Towards a Unified Theory for Texture Modeling. - Song Chun Zhu; Ying Nian Wu; David Mumford (Int. J. Comput. Vis., 1998)

Equivalence of Julesz and Gibbs Texture Ensembles. - Ying Nian Wu; Song Chun Zhu; Xiuwen Liu (ICCV, 1999)

From local features to global perception - A perspective of Gestalt psychology from Markov random field theory. - Song Chun Zhu; Ying Nian Wu (Neurocomputing, 1999)

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

Equivalence of Julesz Ensembles and FRAME Models. - Ying Nian Wu; Song Chun Zhu; Xiuwen Liu (Int. J. Comput. Vis., 2000)

Exploring Texture Ensembles by Efficient Markov Chain Monte Carlo-Toward a 'Trichromacy' Theory of Texture. - Song Chun Zhu; Xiuwen Liu; Ying Nian Wu (IEEE Trans. Pattern Anal. Mach. Intell., 2000)

Dynamic Texture Recognition. - Payam Saisan; Gianfranco Doretto; Ying Nian Wu; Stefano Soatto (CVPR (2), 2001)

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

Dynamic Textures. - Stefano Soatto; Gianfranco Doretto; Ying Nian Wu (ICCV, 2001)

Order Parameters for Detecting Target Curves in Images: When Does High Level Knowledge Help? - Alan L. Yuille; James M. Coughlan; Ying Nian Wu; Song Chun Zhu (Int. J. Comput. Vis., 2001)

Statistical Modeling of Texture Sketch. - Ying Nian Wu; Song Chun Zhu; Cheng-en Guo (ECCV (3), 2002)

What Are Textons? - Song Chun Zhu; Cheng-en Guo; Ying Nian Wu; Yizhou Wang 001 (ECCV (4), 2002)

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

Dynamic Textures. - Gianfranco Doretto; Alessandro Chiuso; Ying Nian Wu; Stefano Soatto (Int. J. Comput. Vis., 2003)

Modeling Visual Patterns by Integrating Descriptive and Generative Methods. - Cheng-en Guo; Song Chun Zhu; Ying Nian Wu (Int. J. Comput. Vis., 2003)

Information Scaling Laws in Natural Scenes. - Cheng-en Guo; Ying Nian Wu; Song Chun Zhu (CVPR Workshops, 2004)

Deformable Template As Active Basis. - Ying Nian Wu; Zhangzhang Si; Chuck Fleming; Song Chun Zhu (ICCV, 2007)

A null space method for over-complete blind source separation. - Ray-Bing Chen; Ying Nian Wu (Comput. Stat. Data Anal., 2007)

Primal sketch: Integrating structure and texture. - Cheng-en Guo; Song Chun Zhu; Ying Nian Wu (Comput. Vis. Image Underst., 2007)

Statistical Principles in Image Modeling. - Ying Nian Wu; Jinhui Li; Ziqiang Liu; Song-Chun Zhu (Technometrics, 2007)

Learning mixed templates for object recognition. - Zhangzhang Si; Haifeng Gong; Ying Nian Wu; Song Chun Zhu (CVPR, 2009)

Wavelet, active basis, and shape script: a tour in the sparse land. - Zhangzhang Si; Ying Nian Wu (Multimedia Information Retrieval, 2010)

Learning Active Basis Model for Object Detection and Recognition. - Ying Nian Wu; Zhangzhang Si; Haifeng Gong; Song Chun Zhu (Int. J. Comput. Vis., 2010)

Image representation by active curves. - Wenze Hu; Ying Nian Wu; Song-Chun Zhu (ICCV, 2011)

Stochastic matching pursuit for Bayesian variable selection. - Ray-Bing Chen; Chi-Hsiang Chu; Te-You Lai; Ying Nian Wu (Stat. Comput., 2011)

Methods to Improve Accuracy and Speed for the Quasi-3D Electromagnetic Environment Simulation. - Yuewei Shen; Lin Zhang 009; Yingnian Wu; Lan Mu; Yandong Lv (AsiaSim (3), 2012)

Comparisons of Ray-Tracing and parabolic equation Methods for the Large-Scale Complex electromagnetic Environment simulations. - Yuewei Shen; Lin Zhang 009; Dengkun Liu; Yingnian Wu; Lan Mu; Ralph C. Huntsinger (Int. J. Model. Simul. Sci. Comput., 2012)

Cosegmentation and Cosketch by Unsupervised Learning. - Jifeng Dai; Ying Nian Wu; Jie Zhou 001; Song-Chun Zhu (ICCV, 2013)

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

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

Bayesian Variable Selection for Multi-response Linear Regression. - Wan-Ping Chen; Ying Nian Wu; Ray-Bing Chen (TAAI, 2014)

Layered Logic Classifiers: Exploring the 'And' and 'Or' Relations. - Zhuowen Tu; Piotr Dollár; Yingnian Wu (CoRR, 2014)

Mouse simulation in human-machine interface using kinect and 3 gear systems. - Yingnian Wu; Guojun Yang; Lin Zhang 009 (Int. J. Model. Simul. Sci. Comput., 2014)

Non-negative matrix factorization of multimodal MRI, fMRI and phenotypic data reveals differential changes in default mode subnetworks in ADHD. - 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 (NeuroImage, 2014)

Cross Entropy. - Ying Nian Wu (Computer Vision, A Reference Guide, 2014)

Data Augmentation. - Ying Nian Wu (Computer Vision, A Reference Guide, 2014)

Histogram. - Ying Nian Wu (Computer Vision, A Reference Guide, 2014)

Statistical Independence. - Ying Nian Wu (Computer Vision, A Reference Guide, 2014)

Mining And-Or Graphs for Graph Matching and Object Discovery. - Quanshi Zhang; Ying Nian Wu; Song-Chun Zhu (ICCV, 2015)

Generative Modeling of Convolutional Neural Networks. - Jifeng Dai; Ying Nian Wu (ICLR (Poster), 2015)

Learning FRAME Models Using CNN Filters for Knowledge Visualization. - Yang Lu 006; Song-Chun Zhu; Ying Nian Wu (CoRR, 2015)

Learning Sparse FRAME Models for Natural Image Patterns. - Jianwen Xie; Wenze Hu; Song-Chun Zhu; Ying Nian Wu (Int. J. Comput. Vis., 2015)

Learning FRAME Models Using CNN Filters. - Yang Lu 006; Song-Chun Zhu; Ying Nian Wu (AAAI, 2016)

A Theory of Generative ConvNet. - Jianwen Xie; Yang Lu 006; Song-Chun Zhu; Ying Nian Wu (ICML, 2016)

Learning Generative ConvNet with Continuous Latent Factors by Alternating Back-Propagation. - Tian Han 001; Yang Lu 006; Song-Chun Zhu; Ying Nian Wu (CoRR, 2016)

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. - Jianwen Xie; Pamela K. Douglas; Ying Nian Wu; Arthur L. Brody; Ariana E. Anderson (CoRR, 2016)

Cooperative Training of Descriptor and Generator Networks. - Jianwen Xie; Yang Lu 006; Song-Chun Zhu; Ying Nian Wu (CoRR, 2016)

Synthesizing Dynamic Textures and Sounds by Spatial-Temporal Generative ConvNet. - Jianwen Xie; Song-Chun Zhu; Ying Nian Wu (CoRR, 2016)

Multi-Shot Mining Semantic Part Concepts in CNNs. - Quanshi Zhang; Ruiming Cao; Ying Nian Wu; Song-Chun Zhu (CoRR, 2016)

Bayesian variable selection for finite mixture model of linear regressions. - Kuo-Jung Lee; Ray-Bing Chen; Ying Nian Wu (Comput. Stat. Data Anal., 2016)

Modeling method for integration of air command and security process. - Yongliang Luo; Yingnian Wu; Yuanhui Qin; Lin Zhang 009; Yuanming Wang (Int. J. Model. Simul. Sci. Comput., 2016)

Alternating Back-Propagation for Generator Network. - Tian Han 001; Yang Lu 006; Song-Chun Zhu; Ying Nian Wu (AAAI, 2017)

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

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

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

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

Interactively Transferring CNN Patterns for Part Localization. - Quanshi Zhang; Ruiming Cao; Shengming Zhang; Mark Edmonds; Ying Nian Wu; Song-Chun Zhu (CoRR, 2017)

A Cost-Sensitive Visual Question-Answer Framework for Mining a Deep And-OR Object Semantics from Web Images. - Quanshi Zhang; Ying Nian Wu; Song-Chun Zhu (CoRR, 2017)

Learning Multi-grid Generative ConvNets by Minimal Contrastive Divergence. - Ruiqi Gao; Yang Lu 006; Junpei Zhou; Song-Chun Zhu; Ying Nian Wu (CoRR, 2017)

Cooperative Learning of Energy-Based Model and Latent Variable Model via MCMC Teaching. - Jianwen Xie; Yang Lu 006; Ruiqi Gao; Ying Nian Wu (AAAI, 2018)

Interpreting CNN Knowledge via an Explanatory Graph. - Quanshi Zhang; Ruiming Cao; Feng Shi 006; Ying Nian Wu; Song-Chun Zhu (AAAI, 2018)

Learning Generative ConvNets via Multi-Grid Modeling and Sampling. - Ruiqi Gao; Yang Lu 006; Junpei Zhou; Song-Chun Zhu; Ying Nian Wu (CVPR, 2018)

Learning Descriptor Networks for 3D Shape Synthesis and Analysis. - Jianwen Xie; Zilong Zheng; Ruiqi Gao; Wenguan Wang; Song-Chun Zhu; Ying Nian Wu (CVPR, 2018)

Interpretable Convolutional Neural Networks. - Quanshi Zhang; Ying Nian Wu; Song-Chun Zhu (CVPR, 2018)

Learning Multi-view Generator Network for Shared Representation. - Tian Han 001; Xianglei Xing; Ying Nian Wu (ICPR, 2018)

Replicating Active Appearance Model by Generator Network. - Tian Han 001; Jiawen Wu; Ying Nian Wu (IJCAI, 2018)

Cooperative Holistic Scene Understanding: Unifying 3D Object, Layout, and Camera Pose Estimation. - Siyuan Huang 001; Siyuan Qi; Yinxue Xiao; Yixin Zhu 001; Ying Nian Wu; Song-Chun Zhu (NeurIPS, 2018)

Interpreting CNNs via Decision Trees. - Quanshi Zhang; Yu Yang 007; Ying Nian Wu; Song-Chun Zhu (CoRR, 2018)

Network Transplanting. - Quanshi Zhang; Yu Yang 007; Ying Nian Wu; Song-Chun Zhu (CoRR, 2018)

Unsupervised Learning of Neural Networks to Explain Neural Networks. - Quanshi Zhang; Yu Yang 007; Yuchen Liu; Ying Nian Wu; Song-Chun Zhu (CoRR, 2018)

Deformable Generator Network: Unsupervised Disentanglement of Appearance and Geometry. - Xianglei Xing; Ruiqi Gao; Tian Han 001; Song-Chun Zhu; Ying Nian Wu (CoRR, 2018)

Interactive Agent Modeling by Learning to Probe. - Tianmin Shu; Caiming Xiong; Ying Nian Wu; Song-Chun Zhu (CoRR, 2018)

A Tale of Three Probabilistic Families: Discriminative, Descriptive and Generative Models. - Ying Nian Wu; Ruiqi Gao; Tian Han 001; Song-Chun Zhu (CoRR, 2018)

Learning Grid-like Units with Vector Representation of Self-Position and Matrix Representation of Self-Motion. - Ruiqi Gao; Jianwen Xie; Song-Chun Zhu; Ying Nian Wu (CoRR, 2018)

Mining Interpretable AOG Representations from Convolutional Networks via Active Question Answering. - Quanshi Zhang; Ruiming Cao; Ying Nian Wu; Song-Chun Zhu (CoRR, 2018)

Explanatory Graphs for CNNs. - Quanshi Zhang; Xin Wang 108; Ruiming Cao; Ying Nian Wu; Feng Shi 006; Song-Chun Zhu (CoRR, 2018)

Divergence Triangle for Joint Training of Generator Model, Energy-based Model, and Inference Model. - Tian Han 001; Erik Nijkamp; Xiaolin Fang 002; Mitch Hill; Song-Chun Zhu; Ying Nian Wu (CoRR, 2018)

Mining deep And-Or object structures via cost-sensitive question-answer-based active annotations. - Quanshi Zhang; Ying Nian Wu; Hao Zhang 063; Song-Chun Zhu (Comput. Vis. Image Underst., 2018)

Piano multipitch estimation using sparse coding embedded deep learning. - Xingda Li; Yujing Guan; Yingnian Wu; Zhongbo Zhang (EURASIP J. Audio Speech Music. Process., 2018)

Deep neural network based i-vector mapping for speaker verification using short utterances. - Jinxi Guo; Ning Xu 010; Kailun Qian; Yang Shi; Kaiyuan Xu; Yingnian Wu; Abeer Alwan (Speech Commun., 2018)

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

Divergence Triangle for Joint Training of Generator Model, Energy-Based Model, and Inferential Model. - Tian Han 001; Erik Nijkamp; Xiaolin Fang 002; Mitch Hill; Song-Chun Zhu; Ying Nian Wu (CVPR, 2019)

Unsupervised Disentangling of Appearance and Geometry by Deformable Generator Network. - Xianglei Xing; Tian Han 001; Ruiqi Gao; Song-Chun Zhu; Ying Nian Wu (CVPR, 2019)

Interpreting CNNs via Decision Trees. - Quanshi Zhang; Yu Yang 007; Haotian Ma; Ying Nian Wu (CVPR, 2019)

Multi-Agent Tensor Fusion for Contextual Trajectory Prediction. - Tianyang Zhao; Yifei Xu; Mathew Monfort; Wongun Choi; Chris L. Baker; Yibiao Zhao; Yizhou Wang 001; Ying Nian Wu (CVPR, 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 (ICLR (Poster), 2019)

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

Learning Generator Networks for Dynamic Patterns. - Tian Han 001; Yang Lu 006; Jiawen Wu; Xianglei Xing; Ying Nian Wu (WACV, 2019)

Interpretable CNNs. - Quanshi Zhang; Ying Nian Wu; Song-Chun Zhu (CoRR, 2019)

Network Transplanting (extended abstract). - Quanshi Zhang; Yu Yang 007; Qian Yu; Ying Nian Wu (CoRR, 2019)

Unsupervised Learning of Neural Networks to Explain Neural Networks (extended abstract). - Quanshi Zhang; Yu Yang 007; Ying Nian Wu (CoRR, 2019)

Inducing Sparse Coding and And-Or Grammar from Generator Network. - Xianglei Xing; Song-Chun Zhu; Ying Nian Wu (CoRR, 2019)

Multimodal Conditional Learning with Fast Thinking Policy-like Model and Slow Thinking Planner-like Model. - Jianwen Xie; Zilong Zheng; Xiaolin Fang 002; Song-Chun Zhu; Ying Nian Wu (CoRR, 2019)

Learning Vector Representation of Content and Matrix Representation of Change: Towards a Representational Model of V1. - Ruiqi Gao; Jianwen Xie; Song-Chun Zhu; Ying Nian Wu (CoRR, 2019)

Learning Trajectory Prediction with Continuous Inverse Optimal Control via Langevin Sampling of Energy-Based Models. - Yifei Xu; Tianyang Zhao; Chris L. Baker; Yibiao Zhao; Ying Nian Wu (CoRR, 2019)

On Learning Non-Convergent Short-Run MCMC Toward Energy-Based Model. - Erik Nijkamp; Song-Chun Zhu; Ying Nian Wu (CoRR, 2019)

Neural Architecture Search for Joint Optimization of Predictive Power and Biological Knowledge. - Zijun Zhang; Linqi Zhou; Liangke Gou; Ying Nian Wu (CoRR, 2019)

Towards Interpretable Image Synthesis by Learning Sparsely Connected AND-OR Networks. - Xianglei Xing; Tianfu Wu 001; Song-Chun Zhu; Ying Nian Wu (CoRR, 2019)

Deep Unsupervised Clustering with Clustered Generator Model. - Dandan Zhu 001; Tian Han 001; Linqi Zhou; Xiaokang Yang 001; Ying Nian Wu (CoRR, 2019)

Representation Learning: A Statistical Perspective. - Jianwen Xie; Ruiqi Gao; Erik Nijkamp; Song-Chun Zhu; Ying Nian Wu (CoRR, 2019)

Learning Deep Generative Models with Short Run Inference Dynamics. - Erik Nijkamp; Bo Pang 004; Tian Han 001; Linqi Zhou; Song-Chun Zhu; Ying Nian Wu (CoRR, 2019)

An improved method of optical flow using human body-following wheeled robot. - Yingnian Wu; Qi Yang; Xiaohang Zhou (Int. J. Model. Simul. Sci. Comput., 2019)

Replicating Neuroscience Observations on ML/MF and AM Face Patches by Deep Generative Model. - Tian Han 001; Xianglei Xing; Jiawen Wu; Ying Nian Wu (Neural Comput., 2019)

A tale of two explanations: Enhancing human trust by explaining robot behavior. - Mark Edmonds; Feng Gao 013; Hangxin Liu; Xu Xie 001; Siyuan Qi; Brandon Rothrock; Yixin Zhu 001; Ying Nian Wu; Hongjing Lu; Song-Chun Zhu (Sci. Robotics, 2019)

On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models. - Erik Nijkamp; Mitch Hill; Tian Han 001; 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)

Joint Training of Variational Auto-Encoder and Latent Energy-Based Model. - Tian Han 001; Erik Nijkamp; Linqi Zhou; Bo Pang 004; Song-Chun Zhu; Ying Nian Wu (CVPR, 2020)

Flow Contrastive Estimation of Energy-Based Models. - Ruiqi Gao; Erik Nijkamp; Diederik P. Kingma; Zhen Xu 006; Andrew M. Dai; Ying Nian Wu (CVPR, 2020)

Inducing Hierarchical Compositional Model by Sparsifying Generator Network. - Xianglei Xing; Tianfu Wu 001; Song-Chun Zhu; Ying Nian Wu (CVPR, 2020)

Learning Multi-layer Latent Variable Model via Variational Optimization of Short Run MCMC for Approximate Inference. - Erik Nijkamp; Bo Pang 004; Tian Han 001; Linqi Zhou; Song-Chun Zhu; Ying Nian Wu (ECCV (6), 2020)

Closed Loop Neural-Symbolic Learning via Integrating Neural Perception, Grammar Parsing, and Symbolic Reasoning. - Qing Li 003; Siyuan Huang 001; Yining Hong; Yixin Chen 003; Ying Nian Wu; Song-Chun Zhu (ICML, 2020)

Imposing implicit feasibility constraints on deformable image registration using a statistical generative model. - Yudi Sang; Xianglei Xing; Ying Nian Wu; Dan Ruan (Image Processing, 2020)

Learning Latent Space Energy-Based Prior Model. - Bo Pang 004; Tian Han 001; Erik Nijkamp; Song-Chun Zhu; Ying Nian Wu (NeurIPS, 2020)

Generative PointNet: 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 (CoRR, 2020)

Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense. - Yixin Zhu 001; Tao Gao 004; Lifeng Fan; Siyuan Huang 001; Mark Edmonds; Hangxin Liu; Feng Gao 013; Chi Zhang 017; Siyuan Qi; Ying Nian Wu; Joshua B. Tenenbaum; Song-Chun Zhu (CoRR, 2020)

Learning Energy-based Model with Flow-based Backbone by Neural Transport MCMC. - Erik Nijkamp; Ruiqi Gao; Pavel Sountsov; Srinivas Vasudevan; Bo Pang 004; Song-Chun Zhu; Ying Nian Wu (CoRR, 2020)

A Representational Model of Grid Cells Based on Matrix Lie Algebras. - Ruiqi Gao; Jianwen Xie; Song-Chun Zhu; Ying Nian Wu (CoRR, 2020)

Learning Latent Space Energy-Based Prior Model for Molecule Generation. - Bo Pang 004; Tian Han 001; Ying Nian Wu (CoRR, 2020)

Semi-supervised Learning by Latent Space Energy-Based Model of Symbol-Vector Coupling. - Bo Pang 004; Erik Nijkamp; Jiali Cui; Tian Han 001; Ying Nian Wu (CoRR, 2020)

Cooperative Training of Descriptor and Generator Networks. - Jianwen Xie; Yang Lu 006; Ruiqi Gao; Song-Chun Zhu; Ying Nian Wu (IEEE Trans. Pattern Anal. Mach. Intell., 2020)

Learning Cycle-Consistent Cooperative Networks via Alternating MCMC Teaching for Unsupervised Cross-Domain Translation. - Jianwen Xie; Zilong Zheng; Xiaolin Fang 002; Song-Chun Zhu; Ying Nian Wu (AAAI, 2021)

Robust Transfer Learning with Pretrained Language Models through Adapters. - Wenjuan Han; Bo Pang 004; Ying Nian Wu (ACL/IJCNLP (2), 2021)

SocAoG: Incremental Graph Parsing for Social Relation Inference in Dialogues. - Liang Qiu 001; Yuan Liang 001; Yizhou Zhao; Pan Lu; Baolin Peng; Zhou Yu; Ying Nian Wu; Song-Chun Zhu (ACL/IJCNLP (1), 2021)

A Model Obfuscation Approach to IoT Security. - Yunqi Guo; Zhaowei Tan; Kaiyuan Chen; Songwu Lu; Ying Nian Wu (CNS, 2021)

Trajectory Prediction With Latent Belief Energy-Based Model. - Bo Pang 004; Tianyang Zhao; Xu Xie 001; Ying Nian Wu (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 (CVPR, 2021)

Learning Neural Representation of Camera Pose with Matrix Representation of Pose Shift via View Synthesis. - Yaxuan Zhu; Ruiqi Gao; Siyuan Huang 001; Song-Chun Zhu; Ying Nian Wu (CVPR, 2021)

Generative Text Modeling through Short Run Inference. - Bo Pang 004; Erik Nijkamp; Tian Han 001; Ying Nian Wu (EACL, 2021)

Learning Energy-Based Models by Diffusion Recovery Likelihood. - Ruiqi Gao; Yang Song 011; Ben Poole; Ying Nian Wu; Diederik P. Kingma (ICLR, 2021)

Latent Space Energy-Based Model of Symbol-Vector Coupling for Text Generation and Classification. - Bo Pang 004; Ying Nian Wu (ICML, 2021)

Congestion-aware Multi-agent Trajectory Prediction for Collision Avoidance. - Xu Xie 001; Chi Zhang 017; Yixin Zhu 001; Ying Nian Wu; Song-Chun Zhu (ICRA, 2021)

Planning on a (Risk) Budget: Safe Non-Conservative Planning in Probabilistic Dynamic Environments. - Hung-Jui Huang; Kai-Chi Huang; Michal Cáp; Yibiao Zhao; Ying Nian Wu; Chris L. Baker (ICRA, 2021)

A Study of Local Optima for Learning Feature Interactions using Neural Networks. - Yangzi Guo; Ying Nian Wu; Adrian Barbu (IJCNN, 2021)

SCRIPT: Self-Critic PreTraining of Transformers. - Erik Nijkamp; Bo Pang 004; Ying Nian Wu; Caiming Xiong (NAACL-HLT, 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 (NeurIPS, 2021)

Unsupervised Foreground Extraction via Deep Region Competition. - Peiyu Yu; Sirui Xie; Xiaojian Ma 001; Yixin Zhu 001; Ying Nian Wu; Song-Chun Zhu (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 (NeurIPS, 2021)

HALMA: Humanlike Abstraction Learning Meets Affordance in Rapid Problem Solving. - Sirui Xie; Xiaojian Ma 001; Peiyu Yu; Yixin Zhu 001; Ying Nian Wu; Song-Chun Zhu (CoRR, 2021)

A HINT from Arithmetic: On Systematic Generalization of Perception, Syntax, and Semantics. - Qing Li 003; Siyuan Huang 001; Yining Hong; Yixin Zhu 001; Ying Nian Wu; Song-Chun Zhu (CoRR, 2021)

SAS: Self-Augmented Strategy for Language Model Pre-training. - Yifei Xu; Jingqiao Zhang; Ru He; Liangzhu Ge; Chao Yang; Cheng Yang; Ying Nian Wu (CoRR, 2021)

Proceedings of ICML 2021 Workshop on Theoretic Foundation, Criticism, and Application Trend of Explainable AI. - Quanshi Zhang; Tian Han 001; Lixin Fan; Zhanxing Zhu; Hang Su; Ying Nian Wu; Jie Ren 018; Hao Zhang 063 (CoRR, 2021)

Learning Energy-Based Spatial-Temporal Generative ConvNets for Dynamic Patterns. - Jianwen Xie; Song-Chun Zhu; Ying Nian Wu (IEEE Trans. Pattern Anal. Mach. Intell., 2021)

Mining Interpretable AOG Representations From Convolutional Networks via Active Question Answering. - Quanshi Zhang; Jie Ren 018; Ge Huang; Ruiming Cao; Ying Nian Wu; Song-Chun Zhu (IEEE Trans. Pattern Anal. Mach. Intell., 2021)

Extraction of an Explanatory Graph to Interpret a CNN. - Quanshi Zhang; Xin Wang 108; Ruiming Cao; Ying Nian Wu; Feng Shi 006; Song-Chun Zhu (IEEE Trans. Pattern Anal. Mach. Intell., 2021)

Interpretable CNNs for Object Classification. - Quanshi Zhang; Xin Wang 108; Ying Nian Wu; Huilin Zhou; Song-Chun Zhu (IEEE Trans. Pattern Anal. Mach. Intell., 2021)

Learning V1 Simple Cells with Vector Representation of Local Content and Matrix Representation of Local Motion. - Ruiqi Gao; Jianwen Xie; Siyuan Huang 001; Yufan Ren; 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 (AAAI, 2022)

Deep Generative model with Hierarchical Latent Factors for Time Series Anomaly Detection. - Cristian I. Challu; Peihong Jiang; Ying Nian Wu; Laurent Callot (AISTATS, 2022)

Transform-Retrieve-Generate: Natural Language-Centric Outside-Knowledge Visual Question Answering. - Feng Gao 013; Qing Ping; Govind Thattai; Aishwarya N. Reganti; Ying Nian Wu; Prem Natarajan (CVPR, 2022)

Learning Algebraic Representation for Systematic Generalization in Abstract Reasoning. - Chi Zhang 017; Sirui Xie; Baoxiong Jia; Ying Nian Wu; Song-Chun Zhu; Yixin Zhu 001 (ECCV (39), 2022)

MCMC Should Mix: Learning Energy-Based Model with Neural Transport Latent Space MCMC. - Erik Nijkamp; Ruiqi Gao; Pavel Sountsov; Srinivas Vasudevan; Bo Pang 004; Song-Chun Zhu; Ying Nian Wu (ICLR, 2022)

Latent Diffusion Energy-Based Model for Interpretable Text Modelling. - Peiyu Yu; Sirui Xie; Xiaojian Ma 001; Baoxiong Jia; Bo Pang 004; Ruiqi Gao; Yixin Zhu 001; Song-Chun Zhu; Ying Nian Wu (ICML, 2022)

Conformal Isometry of Lie Group Representation in Recurrent Network of Grid Cells. - Dehong Xu; Ruiqi Gao; Wenhao Zhang 002; Xue-Xin Wei; Ying Nian Wu (NeurReps, 2022)

Translation-equivariant Representation in Recurrent Networks with a Continuous Manifold of Attractors. - Wenhao Zhang 002; Ying Nian Wu; Si Wu 001 (NeurIPS, 2022)

GACSNet: A Lightweight Network for the Noninvasive Blood Glucose Detection. - Rui Yang; Yingnian Wu; Xiaolong Liu; Wenbai Chen (Appl. Artif. Intell., 2022)

A Thousand Words Are Worth More Than a Picture: Natural Language-Centric Outside-Knowledge Visual Question Answering. - Feng Gao 013; Qing Ping; Govind Thattai; Aishwarya N. Reganti; Ying Nian Wu; Prem Natarajan (CoRR, 2022)

Latent Diffusion Energy-Based Model for Interpretable Text Modeling. - Peiyu Yu; Sirui Xie; Xiaojian Ma 001; Baoxiong Jia; Bo Pang 004; Ruiqi Gao; Yixin Zhu 001; Song-Chun Zhu; Ying Nian Wu (CoRR, 2022)

SpectraNet: Multivariate Forecasting and Imputation under Distribution Shifts and Missing Data. - Cristian Challu; Peihong Jiang; Ying Nian Wu; Laurent Callot (CoRR, 2022)

Cooperative Training of Fast Thinking Initializer and Slow Thinking Solver for Conditional Learning. - Jianwen Xie; Zilong Zheng; Xiaolin Fang 002; Song-Chun Zhu; Ying Nian Wu (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Generative VoxelNet: Learning Energy-Based Models for 3D Shape Synthesis and Analysis. - Jianwen Xie; Zilong Zheng; Ruiqi Gao; Wenguan Wang; Song-Chun Zhu; Ying Nian Wu (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Deformable Generator Networks: Unsupervised Disentanglement of Appearance and Geometry. - Xianglei Xing; Ruiqi Gao; Tian Han 001; Song-Chun Zhu; Ying Nian Wu (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

In situ bidirectional human-robot value alignment. - Luyao Yuan; Xiaofeng Gao 002; Zilong Zheng; Mark Edmonds; Ying Nian Wu; Federico Rossano; Hongjing Lu; Yixin Zhu 001; Song-Chun Zhu (Sci. Robotics, 2022)

Learning Joint Latent Space EBM Prior Model for Multi-layer Generator. - Jiali Cui; Ying Nian Wu; Tian Han 001 (CVPR, 2023)

Sim2Plan: Robot Motion Planning via Message Passing Between Simulation and Reality. - Yizhou Zhao; Yuanhong Zeng; Qian Long; Ying Nian Wu; Song-Chun Zhu (FTC (1), 2023)

Learning Hierarchical Features with Joint Latent Space Energy-Based Prior. - Jiali Cui; Ying Nian Wu; Tian Han 001 (ICCV, 2023)

A Minimalist Dataset for Systematic Generalization of Perception, Syntax, and Semantics. - Qing Li 003; Siyuan Huang 001; Yining Hong; Yixin Zhu 001; Ying Nian Wu; Song-Chun Zhu (ICLR, 2023)

Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning. - Pan Lu; Liang Qiu 001; Kai-Wei Chang; Ying Nian Wu; Song-Chun Zhu; Tanmay Rajpurohit; Peter Clark; Ashwin Kalyan (ICLR, 2023)

On the Complexity of Bayesian Generalization. - Yu-Zhe Shi; Manjie Xu; John E. Hopcroft; Kun He 001; Joshua B. Tenenbaum; Song-Chun Zhu; Ying Nian Wu; Wenjuan Han; Yixin Zhu 001 (ICML, 2023)

Diverse and Faithful Knowledge-Grounded Dialogue Generation via Sequential Posterior Inference. - Yan Xu 012; Deqian Kong; Dehong Xu; Ziwei Ji 001; Bo Pang 004; Pascale Fung; Ying Nian Wu (ICML, 2023)

Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models. - Pan Lu; Baolin Peng; Hao Cheng 002; Michel Galley; Kai-Wei Chang; Ying Nian Wu; Song-Chun Zhu; Jianfeng Gao 001 (NeurIPS, 2023)

Learning Energy-Based Prior Model with Diffusion-Amortized MCMC. - Peiyu Yu; Yaxuan Zhu; Sirui Xie; Xiaojian Ma 001; Ruiqi Gao; Song-Chun Zhu; Ying Nian Wu (NeurIPS, 2023)

A Recurrent Neural Circuit Mechanism of Temporal-scaling Equivariant Representation. - Junfeng Zuo; Xiao Liu; Ying Nian Wu; Si Wu 001; Wenhao Zhang 002 (NeurIPS, 2023)

Network Transplanting for the Functionally Modular Architecture. - Quanshi Zhang; Xu Cheng 005; Xin Wang 108; Yu Yang 007; Yingnian Wu (PRCV (3), 2023)

Molecule Design by Latent Space Energy-Based Modeling and Gradual Distribution Shifting. - Deqian Kong; Bo Pang 004; Tian Han 001; Ying Nian Wu (UAI, 2023)

Learning for Open-World Calibration with Graph Neural Networks. - Qin Zhang; Dongsheng An; Tianjun Xiao; Tong He 002; Qingming Tang; Ying Nian Wu; Joseph Tighe; Yifan Xing (CoRR, 2023)

Progressive Energy-Based Cooperative Learning for Multi-Domain Image-to-Image Translation. - Weinan Song; Yaxuan Zhu; Lei He 001; Yingnian Wu; Jianwen Xie (CoRR, 2023)

Calibration-Aware Margin Loss: Pushing the Accuracy-Calibration Consistency Pareto Frontier for Deep Metric Learning. - Qin Zhang; Linghan Xu; Qingming Tang; Jun Fang; Ying Nian Wu; Joe Tighe; Yifan Xing (CoRR, 2023)

Triple Regression for Camera Agnostic Sim2Real Robot Grasping and Manipulation Tasks. - Yuanhong Zeng; Yizhou Zhao; Ying Nian Wu (CoRR, 2023)

Molecule Design by Latent Prompt Transformer. - Deqian Kong; Yuhao Huang; Jianwen Xie; Ying Nian Wu (CoRR, 2023)

Learning Concept-Based Visual Causal Transition and Symbolic Reasoning for Visual Planning. - Yilue Qian; Peiyu Yu; Ying Nian Wu; Wei Wang 115; Lifeng Fan (CoRR, 2023)

Conformal Normalization in Recurrent Neural Network of Grid Cells. - Dehong Xu; Ruiqi Gao; Wenhao Zhang 002; Xue-Xin Wei; Ying Nian Wu (CoRR, 2023)

Aligner: One Global Token is Worth Millions of Parameters When Aligning Large Language Models. - Ziheng Zhou; Yingnian Wu; Song-Chun Zhu; Demetri Terzopoulos (CoRR, 2023)

Study on a hybrid algorithm combining enhanced ant colony optimization and double improved simulated annealing via clustering in the Traveling Salesman Problem (TSP). - Tan Hao; Ying Nian Wu; Zhang Jiaxing; Zhang Jing (PeerJ Comput. Sci., 2023)

Research on Real-Time Robust Optimization of Perishable Supply-Chain Systems Based on Digital Twins. - Yingnian Wu; Jing Zhang 111; Qingkui Li; Hao Tan (Sensors, 2023)

Production Change Optimization Model of Nonlinear Supply Chain System under Emergencies. - Jing Zhang 111; Yingnian Wu; Qingkui Li (Sensors, 2023)

Energy-Based Continuous Inverse Optimal Control. - Yifei Xu; Jianwen Xie; Tianyang Zhao; Chris L. Baker; Yibiao Zhao; Ying Nian Wu (IEEE Trans. Neural Networks Learn. Syst., 2023)

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 (AAAI, 2024)

Learning for Transductive Threshold Calibration in Open-World Recognition. - Qin Zhang; Dongsheng An; Tianjun Xiao; Tong He 002; Qingming Tang; Ying Nian Wu; Joseph Tighe; Yifan Xing (CVPR, 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 013 (ECCV (87), 2024)

Object-Conditioned Energy-Based Attention Map Alignment in Text-to-Image Diffusion Models. - Yasi Zhang; Peiyu Yu; Ying Nian Wu (ECCV (42), 2024)

Long-Term Social Interaction Context: The Key to Egocentric Addressee Detection. - Deqian Kong; Furqan Khan; Xu Zhang; Prateek Singhal; Ying Nian Wu (ICASSP, 2024)

Neural-Symbolic Recursive Machine for Systematic Generalization. - Qing Li 003; Yixin Zhu 001; Yitao Liang; Ying Nian Wu; Song-Chun Zhu; Siyuan Huang 001 (ICLR, 2024)

Image Translation as Diffusion Visual Programmers. - Cheng Han 001; James Chenhao Liang; Qifan Wang 001; Majid Rabbani; Sohail A. Dianat; Raghuveer Rao; Ying Nian Wu; Dongfang Liu (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 (ICLR, 2024)

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

Learning Concept-Based Causal Transition and Symbolic Reasoning for Visual Planning. - Yilue Qian; Peiyu Yu; Ying Nian Wu; Yao Su 001; Wei Wang 115; Lifeng Fan (IROS, 2024)

LLM3: Large Language Model-based Task and Motion Planning with Motion Failure Reasoning. - Shu Wang 002; Muzhi Han; Ziyuan Jiao; Zeyu Zhang 001; Ying Nian Wu; Song-Chun Zhu; Hangxin Liu (IROS, 2024)

Differentiable VQ-VAE's for Robust White Matter Streamline Encodings. - Andrew Lizarraga; Brandon Taraku; Edouardo Honig; Ying Nian Wu; Shantanu H. Joshi (ISBI, 2024)

NP-NDS: A Nature-Powered Nonlinear Dynamical System for Power Grid Forecasting. - Chunshu Wu; Ruibing Song; Chuan Liu 001; Yuqing Wang; Yousu Chen; Ang Li 006; Dongfang Liu; Ying Nian Wu; Michael Huang; Tong Geng (LoG, 2024)

Atlas3D: Physically Constrained Self-Supporting Text-to-3D for Simulation and Fabrication. - Yunuo Chen 001; Tianyi Xie; Zeshun Zong; Xuan Li; Feng Gao 013; Yin Yang 002; Ying Nian Wu; Chenfanfu Jiang (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 001; Ying Nian Wu (NeurIPS, 2024)

Latent Plan Transformer for Trajectory Abstraction: Planning as Latent Space Inference. - Deqian Kong; Dehong Xu; Minglu Zhao; Bo Pang 004; Jianwen Xie; Andrew Lizarraga; Yuhao Huang; Sirui Xie; 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 P. Murphy; Tim Salimans; Ben Poole; Ruiqi Gao (NeurIPS, 2024)

Visual Fourier Prompt Tuning. - Runjia Zeng; Cheng Han 001; Qifan Wang 001; Chunshu Wu; Tong Geng; Lifu Huang; Ying Nian Wu; Dongfang Liu (NeurIPS, 2024)

Flow Priors for Linear Inverse Problems via Iterative Corrupted Trajectory Matching. - Yasi Zhang; Peiyu Yu; Yaxuan Zhu; Yingshan Chang; Feng Gao 013; Ying Nian Wu; Oscar Leong (NeurIPS, 2024)

The motion planning neural circuit in goal-directed navigation as Lie group operator search. - Junfeng Zuo; Ying Nian Wu; Si Wu 001; Wenhao Zhang 002 (NeurIPS, 2024)

INTERPRET: Interactive Predicate Learning from Language Feedback for Generalizable Task Planning. - Muzhi Han; Yifeng Zhu; Song-Chun Zhu; Ying Nian Wu; Yuke Zhu (Robotics: Science and Systems, 2024)

Latent Plan Transformer: Planning as Latent Variable Inference. - Deqian Kong; Dehong Xu; Minglu Zhao; Bo Pang 004; Jianwen Xie; Andrew Lizarraga; Yuhao Huang; Sirui Xie; Ying Nian Wu (CoRR, 2024)

Efficient and Scalable Fine-Tune of Language Models for Genome Understanding. - Huixin Zhan; Ying Nian Wu; Zijun Zhang (CoRR, 2024)

Dual-Space Optimization: Improved Molecule Sequence 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 001; Ying Nian Wu (CoRR, 2024)

LLM3: Large Language Model-based Task and Motion Planning with Motion Failure Reasoning. - Shu Wang 002; Muzhi Han; Ziyuan Jiao; Zeyu Zhang 001; Ying Nian Wu; Song-Chun Zhu; Hangxin Liu (CoRR, 2024)

Watermarking Generative Tabular Data. - Hengzhi He; Peiyu Yu; Junpeng Ren; Ying Nian Wu; Guang Cheng 003 (CoRR, 2024)

Latent Energy-Based Odyssey: Black-Box Optimization via Expanded Exploration in the Energy-Based Latent Space. - Peiyu Yu; Dinghuai Zhang; Hengzhi He; Xiaojian Ma 001; Ruiyao Miao; Yifan Lu; Yasi Zhang; Deqian Kong; Ruiqi Gao; Jianwen Xie; Guang Cheng 003; Ying Nian Wu (CoRR, 2024)

An Investigation of Conformal Isometry Hypothesis for Grid Cells. - Dehong Xu; Ruiqi Gao; Wenhao Zhang 002; Xue-Xin Wei; Ying Nian Wu (CoRR, 2024)

Inertial Confinement Fusion Forecasting via LLMs. - Mingkai Chen; Taowen Wang; James Chenhao Liang; Chuan Liu 001; Chunshu Wu; Qifan Wang 001; Ying Nian Wu; Michael Huang; Chuang Ren; Ang Li 006; Tong Geng; Dongfang Liu (CoRR, 2024)

Diff-PIC: Revolutionizing Particle-In-Cell Simulation for Advancing Nuclear Fusion with Diffusion Models. - Chuan Liu 001; Chunshu Wu; Shihui Cao; Mingkai Chen; James Chenhao Liang; Ang Li 006; Michael Huang; Chuang Ren; Dongfang Liu; Ying Nian Wu; Tong Geng (CoRR, 2024)

Visual Agents as Fast and Slow Thinkers. - Guangyan Sun; Mingyu Jin; Zhenting Wang; Cheng-Long Wang; Siqi Ma; Qifan Wang 001; Ying Nian Wu; Yongfeng Zhang; Dongfang Liu (CoRR, 2024)

Think Twice Before You Act: Improving Inverse Problem Solving With MCMC. - Yaxuan Zhu; Zehao Dou; Haoxin Zheng; Yasi Zhang; Ying Nian Wu; Ruiqi Gao (CoRR, 2024)

Long-range gene expression prediction with token alignment of large language model. - Edouardo Honig; Huixin Zhan; Ying Nian Wu; Zijun Frank Zhang (CoRR, 2024)

DODT: Enhanced Online Decision Transformer Learning through Dreamer's Actor-Critic Trajectory Forecasting. - Eric Hanchen Jiang; Zhi Zhang 012; Dinghuai Zhang; Andrew Lizarraga; Chenheng Xu; Yasi Zhang; Siyan Zhao; Zhengjie Xu; Peiyu Yu; Yuer Tang; Deqian Kong; Ying Nian Wu (CoRR, 2024)

SlowFast-VGen: Slow-Fast Learning for Action-Driven Long Video Generation. - Yining Hong; Beide Liu; Maxine Wu; Yuanhao Zhai 001; Kai-Wei Chang; Lingjie Li; Kevin Lin; Chung-Ching Lin; Jianfeng Wang; Zhengyuan Yang; Yingnian Wu; Lijuan Wang (CoRR, 2024)

A minimalistic representation model for head direction system. - Minglu Zhao; Dehong Xu; Deqian Kong; Wenhao Zhang 002; Ying Nian Wu (CoRR, 2024)

Quantifying Preferences of Vision-Language Models via Value Decomposition in Social Media Contexts. - Jingxuan Li; Yuning Yang; Shengqi Yang; Yizhou Zhao; Ying Nian Wu (CoRR, 2024)

Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory. - Eric Hanchen Jiang; Yasi Zhang; Zhi Zhang 012; Yixin Wan; Andrew Lizarraga; Shufan Li; Ying Nian Wu (CoRR, 2024)

Learning the Evolution of Physical Structure of Galaxies via Diffusion Models. - Andrew Lizarraga; Eric Hanchen Jiang; Jacob Nowack; Yun Qi Li; Ying Nian Wu; Bernie Boscoe; Tuan Do (CoRR, 2024)

TeamCraft: A Benchmark for Multi-Modal Multi-Agent Systems in Minecraft. - Qian Long; Zhi Li; Ran Gong; Ying Nian Wu; Demetri Terzopoulos; Xiaofeng Gao 002 (CoRR, 2024)

Research on optimization of supply chain inventory system under contingency conditions. - Yingnian Wu; Tan Hao; Zhang Jing; Wang Ding; Wang Hao (RAIRO Oper. Res., 2024)

T-S fuzzy model based event-triggered change control for product and supply chain systems. - Zhibo Wei; Yongze Liu; Yingnian Wu; Wenbai Chen; Qing-Kui Li (Int. J. Syst. Sci., 2024)

Projection transformation-based link prediction for possible collaboration connections in bimodal supply chain networks. - Yingnian Wu; Shuai Jiao; Hao Tan; Qingkui Li; Jing Zhang 111 (Int. J. Model. Simul. Sci. Comput., 2024)

Monitoring Primitive Interactions During the Training of DNNs. - Jie Ren 018; Xinhao Zheng; Jiyu Liu; Andrew Lizarraga; Ying Nian Wu; Liang Lin; Quanshi Zhang (AAAI, 2025)

Value-Spectrum: Quantifying Preferences of Vision-Language Models via Value Decomposition in Social Media Contexts. - Jingxuan Li; Yuning Yang; Shengqi Yang; Linfan Zhang; Ying Nian Wu (ACL (1), 2025)

Diff-PIC: Revolutionizing Particle-In-Cell Nuclear Fusion Simulation with Diffusion Models. - Chuan Liu 001; Chunshu Wu; Shihui Cao; Mingkai Chen; James Chenhao Liang; Ang Li 006; Michael Huang; Chuang Ren; Ying Nian Wu; Dongfang Liu; Tong Geng (ICLR, 2025)

SlowFast-VGen: Slow-Fast Learning for Action-Driven Long Video Generation. - Yining Hong; Beide Liu; Maxine Wu; Yuanhao Zhai 001; Kai-Wei Chang; Linjie Li; Kevin Lin; Chung-Ching Lin; Jianfeng Wang; Zhengyuan Yang; Ying Nian Wu; Lijuan Wang (ICLR, 2025)

DS-LLM: Leveraging Dynamical Systems to Enhance Both Training and Inference of Large Language Models. - Ruibing Song; Chuan Liu 001; Chunshu Wu; Ang Li 006; Dongfang Liu; Ying Nian Wu; Tong Geng (ICLR, 2025)

Visual Agents as Fast and Slow Thinkers. - Guangyan Sun; Mingyu Jin; Zhenting Wang; Cheng-Long Wang; Siqi Ma; Qifan Wang 001; Tong Geng; Ying Nian Wu; Yongfeng Zhang; Dongfang Liu (ICLR, 2025)

On Conformal Isometry of Grid Cells: Learning Distance-Preserving Position Embedding. - Dehong Xu; Ruiqi Gao; Wenhao Zhang 002; Xue-Xin Wei; Ying Nian Wu (ICLR, 2025)

Explore the Reasoning Capability of LLMs in the Chess Testbed. - Shu Wang 002; Lei Ji 001; Renxi Wang; Wenxiao Zhao; Haokun Liu; Yifan Hou; Ying Nian Wu (NAACL (Short Papers), 2025)

On the Analysis and Distillation of Emergent Outlier Properties in Pre-trained Language Models. - Tianyang Zhao; Kunwar Yashraj Singh; Srikar Appalaraju; Peng Tang; Ying Nian Wu; Li Erran Li (NAACL (Long Papers), 2025)

Application of SVM Based on Optimization of Newton Raphson's Algorithm in Non-Invasive Blood Glucose Detection. - Yingnian Wu; Meiqi Sheng; Ding Wang; Shiwei Gao; Hao Tan (Int. J. Imaging Syst. Technol., 2025)

Digital image stabilization based on time-varying filtering empirical mode decomposition and probability density function. - Shiwei Gao; Yingnian Wu; Hao Tan; Hao Wang; Wenbai Chen (J. Electronic Imaging, 2025)

The adaptive two-stage ant colony Simulated Annealing Algorithm for solving the Traveling Salesman Problem. - Yingnian Wu; Hao Wang; Manhua Li; Hao Tan; Ding Wang; Meiqi Sheng (RAIRO Oper. Res., 2025)

Latent Space Energy-based Neural ODEs. - Sheng Cheng; Deqian Kong; Jianwen Xie; Kookjin Lee; Ying Nian Wu; Yezhou Yang (Trans. Mach. Learn. Res., 2025)

Better Prompt Compression Without Multi-Layer Perceptrons. - Edouardo Honig; Andrew Lizarraga; Zijun Frank Zhang; Ying Nian Wu (CoRR, 2025)

Scalable Language Models with Posterior Inference of Latent Thought Vectors. - Deqian Kong; Minglu Zhao; Dehong Xu; Bo Pang 004; Shu Wang 002; Edouardo Honig; Zhangzhang Si; Chuan Li; Jianwen Xie; Sirui Xie; Ying Nian Wu (CoRR, 2025)

Denoising Score Distillation: From Noisy Diffusion Pretraining to One-Step High-Quality Generation. - Tianyu Chen; Yasi Zhang; Zhendong Wang; Ying Nian Wu; Oscar Leong; Mingyuan Zhou (CoRR, 2025)

Latent Adaptive Planner for Dynamic Manipulation. - Donghun Noh; Deqian Kong; Minglu Zhao; Andrew Lizarraga; Jianwen Xie; Ying Nian Wu; Dennis W. Hong (CoRR, 2025)

Seek in the Dark: Reasoning via Test-Time Instance-Level Policy Gradient in Latent Space. - Hengli Li; Chenxi Li; Tong Wu; Xuekai Zhu; Yuxuan Wang; Zhaoxin Yu; Eric Hanchen Jiang; Song-Chun Zhu; Zixia Jia; Ying Nian Wu; Zilong Zheng (CoRR, 2025)

Restoration Score Distillation: From Corrupted Diffusion Pretraining to One-Step High-Quality Generation. - Yasi Zhang; Tianyu Chen; Zhendong Wang; Ying Nian Wu; Mingyuan Zhou; Oscar Leong (CoRR, 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 003; Ali Caner Türkmen; Cuixiong Hu; Huzefa Rangwala; Ying Nian Wu; Yuyang Wang 001; et al. (CoRR, 2025)

Place Cells as Position Embeddings of Multi-Time Random Walk Transition Kernels for Path Planning. - Minglu Zhao; Dehong Xu; Deqian Kong; Wenhao Zhang 002; Ying Nian Wu (CoRR, 2025)

Learning to Rank Chain-of-Thought: An Energy-Based Approach with Outcome Supervision. - Eric Hanchen Jiang; Haozheng Luo; Shengyuan Pang; Xiaomin Li; Zhenting Qi; Hengli Li; Cheng-Fu Yang; Zongyu Lin; Xinfeng Li; Hao Xu; Kai-Wei Chang; Ying Nian Wu (CoRR, 2025)

Discrete Markov Bridge. - Hengli Li; Yuxuan Wang; Song-Chun Zhu; Ying Nian Wu; Zilong Zheng (CoRR, 2025)

FastCache: Fast Caching for Diffusion Transformer Through Learnable Linear Approximation. - Dong Liu; Jiayi Zhang; Yifan Li; Yanxuan Yu; Ben Lengerich; Ying Nian Wu (CoRR, 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 (CoRR, 2025)

Multivariate Long-term Time Series Forecasting with Fourier Neural Filter. - Chenheng Xu; Dan Wu; Yixin Zhu 001; Ying Nian Wu (CoRR, 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 James Wang; Kai-Wei Chang (CoRR, 2025)

PiKV: KV Cache Management System for Mixture of Experts. - Dong Liu; Yanxuan Yu; Ben Lengerich; Ying Nian Wu; Xuhong Wang (CoRR, 2025)

Beyond Pass@1: Self-Play with Variational Problem Synthesis Sustains RLVR. - Xiao Liang; Zhongzhi Li; Yeyun Gong; Yelong Shen; Ying Nian Wu; Zhijiang Guo; Weizhu Chen (CoRR, 2025)