Ying Nian Wu

发表

Deep Unsupervised Clustering with Clustered Generator Model pdf

Xiaokang Yang, Dandan Zhu, Tian Han, 2019, ArXiv.

Minimax Entropy Principle and Its Application to Texture Modeling

Song-Chun Zhu, Ying Nian Wu, David Mumford, 1997, Neural Computation.

Bayesian Variable Selection for Multi-response Linear Regression

Ying Nian Wu, Wan-Ping Chen, Ray-Bin Chen, 2014, TAAI.

Deformable Generator Networks: Unsupervised Disentanglement of Appearance and Geometry

Tian Han, Xianglei Xing, Song-Chun Zhu, 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Dynamic texture recognition

Payam Saisan, Stefano Soatto, Ying Nian Wu, 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

Image representation by active curves

Wenze Hu, Song-Chun Zhu, Ying Nian Wu, 2011, 2011 International Conference on Computer Vision.

Synthesizing Dynamic Patterns by Spatial-Temporal Generative ConvNet

Song-Chun Zhu, Ying Nian Wu, Jianwen Xie, 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Network Transplanting (extended abstract) pdf

Quanshi Zhang, Ying Nian Wu, Yu Yang, 2019, ArXiv.

A Representational Model of Grid Cells' Path Integration Based on Matrix Lie Algebras

Ying Nian Wu, Song-Chun Zhu, Ruiqi Gao, 2020 .

Unsupervised Disentangling of Appearance and Geometry by Deformable Generator Network

Xianglei Xing, Song-Chun Zhu, Ying Nian Wu, 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Visual Learning by Integrating Descriptive and Generative Methods

Song-Chun Zhu, Ying Nian Wu, Cheng-en Guo, 2000, ICCV.

Neural Architecture Search for Joint Optimization of Predictive Power and Biological Knowledge pdf

Linqi Zhou, Ying Nian Wu, Zijun Zhang, 2019, ArXiv.

Multimodal Conditional Learning with Fast Thinking Policy-like Model and Slow Thinking Planner-like Model

Ying Nian Wu, Song-Chun Zhu, Jianwen Xie, 2019, ArXiv.

Semi-supervised Learning by Latent Space Energy-Based Model of Symbol-Vector Coupling pdf

Tian Han, Erik Nijkamp, Ying Nian Wu, 2020, ArXiv.

Learning Inhomogeneous FRAME Models for Object Patterns

Wenze Hu, Song-Chun Zhu, Ying Nian Wu, 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

Learning Energy-Based Models as Generative ConvNets via Multi-grid Modeling and Sampling.

Ying Nian Wu, Song-Chun Zhu, Yang Lu, 2020 .

Stochastic matching pursuit for Bayesian variable selection

Ying Nian Wu, Ray-Bing Chen, Chi-Hsiang Chu, 2011, Stat. Comput..

Motion-Based Generator Model: Unsupervised Disentanglement of Appearance, Trackable and Intrackable Motions in Dynamic Patterns

Song-Chun Zhu, Ying Nian Wu, Ruiqi Gao, 2019, AAAI.

From local features to global perception - A perspective of Gestalt psychology from Markov random field theory

Song-Chun Zhu, Ying Nian Wu, Song-Chun Zhu, 1999, Neurocomputing.

Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense pdf

Joshua B. Tenenbaum, Song-Chun Zhu, Tao Gao, 2020, Engineering.

Unsupervised Learning of Neural Networks to Explain Neural Networks pdf

Quanshi Zhang, Yu Yang, Ying Nian Wu, 2018, ArXiv.

Extracting an Explanatory Graph to Interpret a CNN.

Quanshi Zhang, Ying Nian Wu, Song-Chun Zhu, 2020, IEEE transactions on pattern analysis and machine intelligence.

Inducing Sparse Coding and And-Or Grammar from Generator Network pdf

Xianglei Xing, Song-Chun Zhu, Ying Nian Wu, 2019, ArXiv.

Unbalanced data classification using support vector machines with active learning on scleroderma lung disease patterns

Ying Nian Wu, Jiun-Kae Jack Lee, Hyun J. Kim, 2015 .

Joint Training of Variational Auto-Encoder and Latent Energy-Based Model

Tian Han, Erik Nijkamp, Linqi Zhou, 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Primal sketch: Integrating structure and texture

Song-Chun Zhu, Ying Nian Wu, Cheng-en Guo, 2007, Comput. Vis. Image Underst..

Generative VoxelNet: Learning Energy-Based Models for 3D Shape Synthesis and Analysis

Ying Nian Wu, Song-Chun Zhu, Ruiqi Gao, 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Learning Multi-view Generator Network for Shared Representation

Tian Han, Xianglei Xing, Ying Nian Wu, 2018, 2018 24th International Conference on Pattern Recognition (ICPR).

Interpretable CNNs pdf

Quanshi Zhang, Song-Chun Zhu, Ying Nian Wu, 2019, ArXiv.

Learning Grid-like Units with Vector Representation of Self-Position and Matrix Representation of Self-Motion pdf

Song-Chun Zhu, Ying Nian Wu, Ruiqi Gao, 2018, ArXiv.

Learning Multi-grid Generative ConvNets by Minimal Contrastive Divergence pdf

Yang Lu, Song-Chun Zhu, Ying Nian Wu, 2017, ArXiv.

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

Ying Nian Wu, Jianwen Xie, Pamela K. Douglas, 2016, Journal of Neuroscience Methods.

Wavelet, active basis, and shape script: a tour in the sparse land

Ying Nian Wu, Zhangzhang Si, Y. Wu, 2010, MIR '10.

Interpreting CNNs via Decision Trees

Quanshi Zhang, Yu Yang, Song-Chun Zhu, 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Network Transplanting pdf

Quanshi Zhang, Yu Yang, Song-Chun Zhu, 2018, ArXiv.

Deformable Template As Active Basis

Song-Chun Zhu, Ying Nian Wu, Zhangzhang Si, 2007, 2007 IEEE 11th International Conference on Computer Vision.

Exploring generative perspective of convolutional neural networks by learning random field models

Yang Lu, Song-Chun Zhu, Ying Nian Wu, 2018 .

Closed Loop Neural-Symbolic Learning via Integrating Neural Perception, Grammar Parsing, and Symbolic Reasoning pdf

Qing Li, Ying Nian Wu, Song-Chun Zhu, 2020, ICML.

What are Textons?

Song-Chun Zhu, Ying Nian Wu, Yizhou Wang, 2002, ECCV.

Learning Active Basis Model for Object Detection and Recognition

Song-Chun Zhu, Ying Nian Wu, Haifeng Gong, 2010, International Journal of Computer Vision.

Perceptual Scaling - eScholarship

Song-Chun Zhu, Ying Nian Wu, Cheng-En Guo, 2011 .

Inducing Hierarchical Compositional Model by Sparsifying Generator Network

Xianglei Xing, Ying Nian Wu, Song-Chun Zhu, 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Towards Interpretable Image Synthesis by Learning Sparsely Connected AND-OR Networks pdf

Xianglei Xing, Song-Chun Zhu, Ying Nian Wu, 2019, ArXiv.

Active Basis for Modeling, Learning and Recognizing Deformable Templates

Song-Chun Zhu, Ying Nian Wu, Haifeng Gong, 2008 .

Learning Energy-based Model with Flow-based Backbone by Neural Transport MCMC pdf

Erik Nijkamp, Ying Nian Wu, Song-Chun Zhu, 2020, ArXiv.

Cosegmentation and Cosketch by Unsupervised Learning

Song-Chun Zhu, Ying Nian Wu, Jifeng Dai, 2013, 2013 IEEE International Conference on Computer Vision.

Cooperative Holistic Scene Understanding: Unifying 3D Object, Layout, and Camera Pose Estimation

Song-Chun Zhu, Ying Nian Wu, Yixin Zhu, 2018, NeurIPS.

Statistical Modeling of Texture Sketch

Song-Chun Zhu, Ying Nian Wu, Cheng-en Guo, 2002, ECCV.

Exploring Texture Ensembles by Efficient Markov Chain Monte Carlo-Toward a 'Trichromacy' Theory of Texture

Song-Chun Zhu, Ying Nian Wu, Xiuwen Liu, 2000, IEEE Trans. Pattern Anal. Mach. Intell..

Learning mixed templates for object recognition

Song-Chun Zhu, Ying Nian Wu, Haifeng Gong, 2009, CVPR.

Modeling Visual Patterns by Integrating Descriptive and Generative Methods

Song-Chun Zhu, Ying Nian Wu, Cheng-en Guo, 2004, International Journal of Computer Vision.

Interpretable CNNs for Object Classification

Quanshi Zhang, Ying Nian Wu, Song-Chun Zhu, 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Dynamic Pattern Synthesis by Spatial-Temporal Generative ConvNet

Song-Chun Zhu, Ying Nian Wu, Jianwen Xie, 2018 .

Growing Interpretable Part Graphs on ConvNets via Multi-Shot Learning

Quanshi Zhang, Song-Chun Zhu, Ying Nian Wu, 2016, AAAI.

Order parameters for minimax entropy distributions: when does high level knowledge help?

Alan L. Yuille, Song-Chun Zhu, Ying Nian Wu, 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

Learning Generative ConvNets via Multi-grid Modeling and Sampling

Yang Lu, Song-Chun Zhu, Ying Nian Wu, 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

Emergence of analogy from relation learning

Ying Nian Wu, Hongjing Lu, Keith J. Holyoak, 2019, Proceedings of the National Academy of Sciences.

Learning Dynamic Generator Model by Alternating Back-Propagation Through Time

Song-Chun Zhu, Ying Nian Wu, Ruiqi Gao, 2018, AAAI.

Mining Object Parts from CNNs via Active Question-Answering

Quanshi Zhang, Song-Chun Zhu, Ying Nian Wu, 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

A null space method for over-complete blind source separation

Ying Nian Wu, Ray-Bing Chen, Y. Wu, 2007, Comput. Stat. Data Anal..

Flow Contrastive Estimation of Energy-Based Models

Andrew M. Dai, Erik Nijkamp, Ying Nian Wu, 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Cooperative Learning of Energy-Based Model and Latent Variable Model via MCMC Teaching

Yang Lu, Ying Nian Wu, Ruiqi Gao, 2018, AAAI.

Learning Generator Networks for Dynamic Patterns

Tian Han, Yang Lu, Xianglei Xing, 2019, 2019 IEEE Winter Conference on Applications of Computer Vision (WACV).

Interactive Agent Modeling by Learning to Probe pdf

Song-Chun Zhu, Ying Nian Wu, Tianmin Shu, 2018, ArXiv.

Order Parameters for Detecting Target Curves in Images: When Does High Level Knowledge Help?

Alan L. Yuille, Song-Chun Zhu, Ying Nian Wu, 2004, International Journal of Computer Vision.

Filters, Random Fields and Maximum Entropy (FRAME): Towards a Unified Theory for Texture Modeling

Song-Chun Zhu, Ying Nian Wu, David Mumford, 1998, International Journal of Computer Vision.

A Representational Model of Grid Cells Based on Matrix Lie Algebras pdf

Ying Nian Wu, Song-Chun Zhu, Ruiqi Gao, 2020, ArXiv.

Learning Non-Convergent Non-Persistent Short-Run MCMC Toward Energy-Based Model

Erik Nijkamp, Ying Nian Wu, Song-Chun Zhu, 2019, NeurIPS.

Multi-Agent Tensor Fusion for Contextual Trajectory Prediction

Ying Nian Wu, Wongun Choi, Yizhou Wang, 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Modeling images and textures by minimax entropy

Song-Chun Zhu, Ying Nian Wu, David Mumford, 1997, Electronic Imaging.

Bayesian variable selection for finite mixture model of linear regressions

Ying Nian Wu, Ray-Bing Chen, Kuo-Jung Lee, 2016, Comput. Stat. Data Anal..

Equivalence of Julesz and Gibbs texture ensembles

Song-Chun Zhu, Ying Nian Wu, Xiuwen Liu, 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

Learning Energy-Based Models by Diffusion Recovery Likelihood pdf

Ying Nian Wu, Yang Song, Ruiqi Gao, 2020, ICLR.

Learning Grid Cells as Vector Representation of Self-Position Coupled with Matrix Representation of Self-Motion

Song-Chun Zhu, Ying Nian Wu, Ruiqi Gao, 2018, ICLR.

Mining deep And-Or object structures via cost-sensitive question-answer-based active annotations

Quanshi Zhang, Song-Chun Zhu, Ying Nian Wu, 2017, Comput. Vis. Image Underst..

Learning vector representation of local content and matrix representation of local motion, with implications for V1

Ying Nian Wu, Song-Chun Zhu, Ruiqi Gao, 2019 .

Mining Interpretable AOG Representations From Convolutional Networks via Active Question Answering

Quanshi Zhang, Song-Chun Zhu, Ying Nian Wu, 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Replicating Neuroscience Observations on ML/MF and AM Face Patches by Deep Generative Model

Ying Nian Wu, Xianglei Xing, Tian Han, 2019, Neural Computation.

A Theory of Generative ConvNet pdf

Yang Lu, Song-Chun Zhu, Ying Nian Wu, 2016, ICML.

Learning Multi-layer Latent Variable Model with Short Run MCMC Inference Dynamics

Tian Han, Erik Nijkamp, Linqi Zhou, 2019 .

[Optimization Transfer Using Surrogate Objective Functions]: Discussion

Ying Nian Wu, 2000 .

Towards a mathematical theory of primal sketch and sketchability

Song-Chun Zhu, Ying Nian Wu, Cheng-en Guo, 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

Synthesizing Dynamic Textures and Sounds by Spatial-Temporal Generative ConvNet pdf

Song-Chun Zhu, Ying Nian Wu, Jianwen Xie, 2016, ArXiv.

Learning FRAME Models Using CNN Filters

Yang Lu, Song-Chun Zhu, Ying Nian Wu, 2015, AAAI.

Sparse and deep generalizations of the FRAME model

Yang Lu, Song-Chun Zhu, Ying Nian Wu, 2018 .

Information Scaling Laws in Natural Scenes

Song-Chun Zhu, Ying Nian Wu, Cheng-en Guo, 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

Learning Deep Generative Models with Short Run Inference Dynamics pdf

Tian Han, Erik Nijkamp, Song-Chun Zhu, 2019, ArXiv.

Explanatory Graphs for CNNs pdf

Quanshi Zhang, Feng Shi, Xin Wang, 2018, ArXiv.

Cross Entropy

Ying Nian Wu, Yingtong Wu, 2014, Computer Vision, A Reference Guide.

Energy-Based Continuous Inverse Optimal Control

Ying Nian Wu, Jianwen Xie, Tianyang Zhao, 2019 .

Learning Latent Space Energy-Based Prior Model pdf

Tian Han, Erik Nijkamp, Ying Nian Wu, 2020, NeurIPS.

A tale of two explanations: Enhancing human trust by explaining robot behavior

Song-Chun Zhu, Ying Nian Wu, Xu Xie, 2019, Science Robotics.

Data Augmentation

Ying Nian Wu, 2014, Computer Vision, A Reference Guide.

Unsupervised learning of compositional sparse code for natural image representation

Yi Hong, Wenze Hu, Song-Chun Zhu, 2013 .

Generative Modeling of Convolutional Neural Networks pdf

Ying Nian Wu, Jifeng Dai, Y. Wu, 2014, ICLR.

Learning Sparse FRAME Models for Natural Image Patterns

Wenze Hu, Song-Chun Zhu, Ying Nian Wu, 2014, International Journal of Computer Vision.

Mining And-Or Graphs for Graph Matching and Object Discovery

Quanshi Zhang, Song-Chun Zhu, Ying Nian Wu, 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

Learning Energy-Based Spatial-Temporal Generative ConvNets for Dynamic Patterns

Song-Chun Zhu, Ying Nian Wu, Jianwen Xie, 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

A Cost-Sensitive Visual Question-Answer Framework for Mining a Deep And-OR Object Semantics from Web Images pdf

Quanshi Zhang, Song-Chun Zhu, Ying Nian Wu, 2017, ArXiv.

rMATS: Robust and flexible detection of differential alternative splicing from replicate RNA-Seq data

Lan Lin, Ying Nian Wu, Yi Xing, 2014, Proceedings of the National Academy of Sciences.

On the Anatomy of MCMC-based Maximum Likelihood Learning of Energy-Based Models pdf

Tian Han, Erik Nijkamp, Song-Chun Zhu, 2019, AAAI.

Cooperative Training of Descriptor and Generator Networks

Yang Lu, Song-Chun Zhu, Ying Nian Wu, 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

Alan L. Yuille, Ying Nian Wu, Mark S. Cohen, 2014, NeuroImage.

Learning FRAME Models Using CNN Filters for Knowledge Visualization pdf

Yang Lu, Song-Chun Zhu, Ying Nian Wu, 2015, ArXiv.

On Learning Non-Convergent Short-Run MCMC Toward Energy-Based Model pdf

Erik Nijkamp, Song-Chun Zhu, Ying Nian Wu, 2019, ArXiv.

Mining a Deep And-OR Object Semantics from Web Images via Cost-Sensitive Question-Answer-Based Active Annotations

Quanshi Zhang, Song-Chun Zhu, Hao Zhang, 2017 .

Generative PointNet: Energy-Based Learning on Unordered Point Sets for 3D Generation, Reconstruction and Classification pdf

Ying Nian Wu, Song-Chun Zhu, Jianwen Xie, 2020, ArXiv.

Interpretable Convolutional Neural Networks

Quanshi Zhang, Song-Chun Zhu, Ying Nian Wu, 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

Learning Multi-layer Latent Variable Model via Variational Optimization of Short Run MCMC for Approximate Inference

Erik Nijkamp, Linqi Zhou, Ying Nian Wu, 2019, ECCV.

Learning Generative ConvNet with Continuous Latent Factors by Alternating Back-Propagation pdf

Tian Han, Yang Lu, Song-Chun Zhu, 2016, ArXiv.

Deep Learning With TensorFlow: A Review

Erik Nijkamp, Ying Nian Wu, Bo Pang, 2019, Journal of Educational and Behavioral Statistics.

Statistical Principles in Image Modeling

Ziqiang Liu, Song-Chun Zhu, Ying Nian Wu, 2007, Technometrics.

Divergence Triangle for Joint Training of Generator Model, Energy-Based Model, and Inferential Model

Tian Han, Erik Nijkamp, Song-Chun Zhu, 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Representation Learning: A Statistical Perspective pdf

Erik Nijkamp, Song-Chun Zhu, Ying Nian Wu, 2019, Annual Review of Statistics and Its Application.

Alternating Back-Propagation for Generator Network

Tian Han, Yang Lu, Song-Chun Zhu, 2016, AAAI.

FRAME: filters, random fields, and minimax entropy towards a unified theory for texture modeling

Song-Chun Zhu, Ying Nian Wu, David Mumford, 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

A Tale of Three Probabilistic Families: Discriminative, Descriptive and Generative Models pdf

Tian Han, Song-Chun Zhu, Ying Nian Wu, 2018, Quarterly of Applied Mathematics.

Imposing implicit feasibility constraints on deformable image registration using a statistical generative model

Xianglei Xing, Ying Nian Wu, Dan Ruan, 2020, Medical Imaging: Image Processing.

Interactively Transferring CNN Patterns for Part Localization pdf

Quanshi Zhang, Song-Chun Zhu, Ying Nian Wu, 2017, ArXiv.

Unsupervised Learning of Dictionaries of Hierarchical Compositional Models

Wenze Hu, Song-Chun Zhu, Ying Nian Wu, 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

Statistical Independence

Ying Nian Wu, 2014, Computer Vision, A Reference Guide.

Equivalence of Julesz Ensembles and FRAME Models

Song-Chun Zhu, Ying Nian Wu, Xiuwen Liu, 2000, International Journal of Computer Vision.

Dynamic Textures

Stefano Soatto, Alessandro Chiuso, Ying Nian Wu, 2003, International Journal of Computer Vision.

Learning Latent Space Energy-Based Prior Model for Molecule Generation pdf

Tian Han, Ying Nian Wu, Bo Pang, 2020, ArXiv.

Replicating Active Appearance Model by Generator Network pdf

Tian Han, Ying Nian Wu, Jiawen Wu, 2018, IJCAI.

Data Augmentation, Internal Representation, and Unsupervised Learning

Ying Nian Wu, 2011 .

Generative Hierarchical Learning of Sparse FRAME Models

Erik Nijkamp, Song-Chun Zhu, Ying Nian Wu, 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Finding Deep Local Optima Using Network Pruning

Adrian Barbu, Ying Nian Wu, Yiyuan She, 2019 .

Joint Cosegmentation and Cosketch by Unsupervised Learning

Song-Chun Zhu, Ying Nian Wu, Jifeng Dai, 2014 .

Interpreting CNN knowledge via an Explanatory Graph pdf

Quanshi Zhang, Feng Shi, Song-Chun Zhu, 2017, AAAI.

Learning Descriptor Networks for 3D Shape Synthesis and Analysis

Song-Chun Zhu, Ying Nian Wu, Wenguan Wang, 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

Multi-Shot Mining Semantic Part Concepts in CNNs pdf

Quanshi Zhang, Song-Chun Zhu, Ying Nian Wu, 2016, ArXiv.

On Path Integration Model of Grid Cells: Locally Conformal Embedding and Matrix Lie Algebra

Xue-Xin Wei, Ying Nian Wu, Song-Chun Zhu, 2020 .

Learning Energy-Based Models by Diffusion Recovery Likelihood pdf

Ying Nian Wu, Yang Song, Ruiqi Gao, 2021, ICLR 2021 Poster.

Learning Cycle-Consistent Cooperative Networks via Alternating MCMC Teaching for Unsupervised Cross-Domain Translation

Song-Chun Zhu, Ying Nian Wu, Jianwen Xie, 2021, AAAI.

Trajectory Prediction with Latent Belief Energy-Based Model

Ying Nian Wu, Tianyang Zhao, Bo Pang, 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Latent Space Energy-Based Model of Symbol-Vector Coupling for Text Generation and Classification pdf

Ying Nian Wu, Bo Pang, Y. Wu, 2021, ICML.

Proceedings of ICML 2021 Workshop on Theoretic Foundation, Criticism, and Application Trend of Explainable AI pdf

Quanshi Zhang, Zhanxing Zhu, Tian Han, 2021, ArXiv.

Extraction of an Explanatory Graph to Interpret a CNN

Quanshi Zhang, Song-Chun Zhu, Ying Nian Wu, 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.

A HINT from Arithmetic: On Systematic Generalization of Perception, Syntax, and Semantics pdf

Song-Chun Zhu, Ying Nian Wu, Yixin Zhu, 2021, ArXiv.

Iterative Teacher-Aware Learning pdf

Song-Chun Zhu, Ying Nian Wu, Quanquan Gu, 2021, NeurIPS.

SocAoG: Incremental Graph Parsing for Social Relation Inference in Dialogues

Zhou Yu, Song-Chun Zhu, Ying Nian Wu, 2021, ACL/IJCNLP.

Robust Transfer Learning with Pretrained Language Models through Adapters pdf

Ying Nian Wu, Bo Pang, Wenjuan Han, 2021, ACL.

Learning Algebraic Representation for Systematic Generalization in Abstract Reasoning pdf

Song-Chun Zhu, Ying Nian Wu, Sirui Xie, 2021, ArXiv.

SAS: Self-Augmented Strategy for Language Model Pre-training pdf

Ying Nian Wu, Yifei Xu, Liangzhu Ge, 2021, ArXiv.

HALMA: Humanlike Abstraction Learning Meets Affordance in Rapid Problem Solving pdf

Song-Chun Zhu, Ying Nian Wu, Sirui Xie, 2021, ArXiv.

Generative Text Modeling through Short Run Inference

Tian Han, Erik Nijkamp, Ying Nian Wu, 2021, EACL.

Planning on a (Risk) Budget: Safe Non-Conservative Planning in Probabilistic Dynamic Environments

Ying Nian Wu, Yibiao Zhao, Hung-Jui Huang, 2021, 2021 IEEE International Conference on Robotics and Automation (ICRA).

Unsupervised Foreground Extraction via Deep Region Competition pdf

Song-Chun Zhu, Ying Nian Wu, Sirui Xie, 2021, NeurIPS.

Congestion-aware Multi-agent Trajectory Prediction for Collision Avoidance

Song-Chun Zhu, Ying Nian Wu, Xu Xie, 2021, 2021 IEEE International Conference on Robotics and Automation (ICRA).

Joint Training of Variational Auto-Encoder and Latent Energy-Based Model

Tian Han, Erik Nijkamp, Song-Chun Zhu, 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Knowledge Via An Explanatory Graph

Quanshi Zhang, Song-Chun Zhu, Ying Nian Wu, 2017 .

Cooperative Training of Fast Thinking Initializer and Slow Thinking Solver for Multi-Modal Conditional Learning.

Song-Chun Zhu, Ying Nian Wu, Jianwen Xie, 2019 .

Learning Mixtures of Bernoulli Templates by Two-Round EM with Performance Guarantee

Adrian Barbu, Ying Nian Wu, Tianfu Wu, 2013 .

Efficient Algorithms for Robust Estimation in Linear Mixed-Effects Models Using the Multivariate t Distribution

Ying Nian Wu, Y. Wu, Chuanhai Liu, 2001 .