Quanshi Zhang

发表

Unsupervised skeleton extraction and motion capture from 3D deformable matching

Xuan Song, Quanshi Zhang, Ryosuke Shibasaki, 2013, Neurocomputing.

F-040 Vehicle-borne Pedestrian Detection by Fusing Horizontal Laser Data and Video Images

Quanshi Zhang, Ryosuke Shibasaki, Hongbin Zha, 2010 .

Unsupervised Skeleton Extraction and Motion Capture from Kinect Video via 3 D Deformable Matching

R. Shibasaki, Huijing Zhao, Quanshi Zhang, 2012 .

Reports of the Workshops Held at the 2019 AAAI Conference on Artificial Intelligence

Julian Togelius, Quanshi Zhang, Christopher W. Geib, 2019, AI Mag..

When 3 D Reconstruction Meets Ubiquitous RGB-D Images

R. Shibasaki, Huijing Zhao, Quanshi Zhang, 2014 .

RASAT: Integrating Relational Structures into Pretrained Seq2Seq Model for Text-to-SQL

Quanshi Zhang, Chenghu Zhou, Xinbing Wang, 2022, 2205.06983.

Trap of Feature Diversity in the Learning of MLPs

Quanshi Zhang, Kangrui Wang, Dongrui Liu, 2021, ArXiv.

Interpretable Generative Adversarial Networks

Quanshi Zhang, Boyu Diao, Chao Li, 2022, AAAI.

Towards Theoretical Analysis of Transformation Complexity of ReLU DNNs

Quanshi Zhang, Meng Zhou, Shih-Han Chan, 2022, ICML.

Quantifying the Knowledge in a DNN to Explain Knowledge Distillation for Classification

Quanshi Zhang, Yilan Chen, Xu Cheng, 2022, IEEE Transactions on Pattern Analysis and Machine Intelligence.

RASAT: Integrating Relational Structures into Pretrained Seq2Seq Model for Text-to-SQL

Zhouhan Lin, Quanshi Zhang, Chenghu Zhou, 2022, EMNLP.

Quantification and Analysis of Layer-wise and Pixel-wise Information Discarding

Quanshi Zhang, Fan Zhou, Haotian Ma, 2019, ICML.

Towards Axiomatic, Hierarchical, and Symbolic Explanation for Deep Models

Quanshi Zhang, Huiqi Deng, Jie Ren, 2021, ArXiv.

K NOWLEDGE C ONSISTENCY BETWEEN N EURAL N ET WORKS AND B EYOND

Quanshi Zhang, Tianlin Li, Ruofan Liang, 2021 .

Rapid detection and recognition of whole brain activity in a freely behaving Caenorhabditis elegans

Quan Wen, Quanshi Zhang, Tianqi Xu, 2021, PLoS Comput. Biol..

Visual graph mining for graph matching

Xuan Song, Quanshi Zhang, Ryosuke Shibasaki, 2019, Comput. Vis. Image Underst..

Visual interpretability for deep learning: a survey

Quanshi Zhang, Song-Chun Zhu, Song-Chun Zhu, 2018, Frontiers of Information Technology & Electronic Engineering.

Interpretable Convolutional Neural Networks

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

A novel dynamic model for multiple pedestrians tracking in extremely crowded scenarios

Xuan Song, Quanshi Zhang, Ryosuke Shibasaki, 2013, Inf. Fusion.

A fully online and unsupervised system for large and high-density area surveillance: Tracking, semantic scene learning and abnormality detection

H. Zha, R. Shibasaki, J. Cui, 2013, TIST.

Visualizing the Emergence of Intermediate Visual Patterns in DNNs pdf

Quanshi Zhang, Mingjie Li, Shaobo Wang, 2021, NeurIPS.

Prediction of human emergency behavior and their mobility following large-scale disaster

Xuan Song, Quanshi Zhang, Yoshihide Sekimoto, 2014, KDD.

C AN W E F AITHFULLY R EPRESENT A BSENCE S TATES TO C OMPUTE S HAPLEY V ALUES ON A DNN?

Quanshi Zhang, Jie Ren, Qirui Chen, 2023 .

Learning Baseline Values for Shapley Values

Quanshi Zhang, Jie Ren, Qirui Chen, 2021, ArXiv.

Interpreting Representation Quality of DNNs for 3D Point Cloud Processing pdf

Quanshi Zhang, Qihan Ren, Dongrui Liu, 2021, NeurIPS.

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.

Interpreting CNNs via Decision Trees

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

Interpreting CNN knowledge via an Explanatory Graph pdf

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

A Roadmap for Big Model

Ning Ding, Xiaodong He, L. Zhang, 2022, ArXiv.

Interpreting Multivariate Shapley Interactions in DNNs

Quanshi Zhang, Longjie Zheng, Hao Zhang, 2021, AAAI.

Interpreting and Disentangling Feature Components of Various Complexity from DNNs pdf

Quanshi Zhang, Jie Ren, Mingjie Li, 2020, ICML.

Explaining Knowledge Distillation by Quantifying the Knowledge

Quanshi Zhang, Xu Cheng, Yilan Chen, 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

A Unified Game-Theoretic Interpretation of Adversarial Robustness pdf

Quanshi Zhang, Xin Wang, Jie Ren, 2021, ArXiv.

Growing Interpretable Part Graphs on ConvNets via Multi-Shot Learning

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

Multi-Shot Mining Semantic Part Concepts in CNNs pdf

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

Bayesian Neural Networks Tend to Ignore Complex and Sensitive Concepts

Quanshi Zhang, Huiqi Deng, Yunuo Chen, 2023, ArXiv.

3D-Rotation-Equivariant Quaternion Neural Networks

Quanshi Zhang, Wen Shen, Zhihua Wei, 2019, ECCV.

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..

Knowledge Consistency between Neural Networks and Beyond

Quanshi Zhang, Jing Wang, Longfei Li, 2019, ICLR.

Discovering and Explaining the Representation Bottleneck of DNNs

Quanshi Zhang, Huiqi Deng, Jie Ren, 2021, ICLR.

Interpretable CNNs for Object Classification

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

Where We Have Arrived in Proving the Emergence of Sparse Symbolic Concepts in AI Models

Quanshi Zhang, Jiayang Gao, Wen Shen, 2023, 2305.01939.

Technical Note: Defining and Quantifying AND-OR Interactions for Faithful and Concise Explanation of DNNs

Quanshi Zhang, Mingjie Li, 2023, ArXiv.

HarsanyiNet: Computing Accurate Shapley Values in a Single Forward Propagation

Quanshi Zhang, Siyu Lou, Lu Chen, 2023, ICML.

Can the Inference Logic of Large Language Models be Disentangled into Symbolic Concepts?

Quanshi Zhang, Mingjie Li, Wen Shen, 2023, 2304.01083.

Concept-Level Explanation for the Generalization of a DNN

Quanshi Zhang, Shih-Han Chan, Huiqi Deng, 2023, ArXiv.

Does a Neural Network Really Encode Symbolic Concept?

Quanshi Zhang, Mingjie Li, 2023, ICML.

Defects of Convolutional Decoder Networks in Frequency Representation

Quanshi Zhang, Ling Tang, YueFeng Chen, 2022, ArXiv.

Batch Normalization Is Blind to the First and Second Derivatives of the Loss

Quanshi Zhang, Ling Tang, Zhanpeng Zhou, 2022, AAAI.

Defining and Quantifying the Emergence of Sparse Concepts in DNNs

Quanshi Zhang, J. Ren, Huiqi Deng, 2021, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Interpreting Attributions and Interactions of Adversarial Attacks pdf

Quanshi Zhang, Yufei Zhu, Xin Wang, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).

A Game-Theoretic Taxonomy of Visual Concepts in DNNs pdf

Quanshi Zhang, Jie Ren, Yi Zheng, 2021, ArXiv.

Building Interpretable Interaction Trees for Deep NLP Models

Quanshi Zhang, Mengyue Wu, Ruizhao Chen, 2021, AAAI.

A Unified Approach to Interpreting and Boosting Adversarial Transferability pdf

Quanshi Zhang, Xiangming Zhu, Jie Ren, 2020, ICLR.

Interpreting and Boosting Dropout from a Game-Theoretic View

Hao Zhang, Quanshi Zhang, Mingjie Li, 2020, ICLR.

Game-theoretic Understanding of Adversarially Learned Features

Xin Wang, Quanshi Zhang, Meng Zhou, 2021, ArXiv.

Understanding and Unifying Fourteen Attribution Methods with Taylor Interactions

Quanshi Zhang, Mengnan Du, Weifu Chen, 2023, arXiv.org.

Proving Common Mechanisms Shared by Twelve Methods of Boosting Adversarial Transferability

Quanshi Zhang, Yisen Wang, Jie Ren, 2022, arXiv.org.

A Hypothesis for the Aesthetic Appreciation in Neural Networks pdf

Quanshi Zhang, Xin Wang, Xu Cheng, 2021, ArXiv.

Towards a Unified Game-Theoretic View of Adversarial Perturbations and Robustness

Xin Wang, Quanshi Zhang, Meng Zhou, 2021, Neural Information Processing Systems.

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.

Moving object classification using horizontal laser scan data

Quanshi Zhang, Ryosuke Shibasaki, Hongbin Zha, 2009, 2009 IEEE International Conference on Robotics and Automation.

Examining CNN representations with respect to Dataset Bias

Quanshi Zhang, Song-Chun Zhu, Wenguan Wang, 2017, AAAI.

When 3D Reconstruction Meets Ubiquitous RGB-D Images

Xuan Song, Quanshi Zhang, Ryosuke Shibasaki, 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

Intelligent System for Human Behavior Analysis and Reasoning Following Large-Scale Disasters

Xuan Song, Quanshi Zhang, Yoshihide Sekimoto, 2013, IEEE Intelligent Systems.

Can We Faithfully Represent Masked States to Compute Shapley Values on a DNN?

Quanshi Zhang, J. Ren, Qirui Chen, 2021, 2105.10719.

Prediction and Simulation of Human Mobility Following Natural Disasters

Xuan Song, Quanshi Zhang, Yoshihide Sekimoto, 2016, ACM Trans. Intell. Syst. Technol..

Interpretable Compositional Convolutional Neural Networks pdf

Quanshi Zhang, Zhihua Wei, Shikun Huang, 2021, IJCAI.

D ISCOVERING AND E XPLAINING THE R EPRESENTA-TION B OTTLENECK OF DNN S

Quanshi Zhang, Huiqi Deng, Qihan Ren, 2021 .

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.

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 .

Modeling and probabilistic reasoning of population evacuation during large-scale disaster

Xuan Song, Quanshi Zhang, Yoshihide Sekimoto, 2013, KDD.

Interpreting Hierarchical Linguistic Interactions in DNNs pdf

Quanshi Zhang, Hao Zhang, Xu Cheng, 2020, ArXiv.

Towards a Deep and Unified Understanding of Deep Neural Models in NLP

Quanshi Zhang, Xing Xie, Di He, 2019, ICML.

Intelligent System for Urban Emergency Management during Large-Scale Disaster

Xuan Song, Quanshi Zhang, Yoshihide Sekimoto, 2014, AAAI.

Achieving adversarial robustness via sparsity pdf

Quanshi Zhang, Shufan Wang, Liyao Xiang, 2020, Machine Learning.

Verifiability and Predictability: Interpreting Utilities of Network Architectures for Point Cloud Processing

Quanshi Zhang, Zhihua Wei, Shikun Huang, 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Towards a Unified Evaluation of Explanation Methods without Ground Truth pdf

Quanshi Zhang, Hao Zhang, Jiayi Chen, 2019, ArXiv.

Utility Analysis of Network Architectures for 3D Point Cloud Processing

Quanshi Zhang, Zhihua Wei, Wen Shen, 2019, ArXiv.

Knowledge Isomorphism between Neural Networks

Longfei Li, Quanshi Zhang, Tianlin Li, 2019, ArXiv.

Quantifying Layerwise Information Discarding of Neural Networks pdf

Quanshi Zhang, Fan Zhou, Haotian Ma, 2019, ArXiv.

Explaining Neural Networks Semantically and Quantitatively

Quanshi Zhang, Hao Chen, Jie Ren, 2018, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

Explaining AlphaGo: Interpreting Contextual Effects in Neural Networks pdf

Quanshi Zhang, Song-Chun Zhu, Robert C. Qiu, 2018, ArXiv.

Exploring Image Regions Not Well Encoded by an INN

Quanshi Zhang, Zenan Ling, Fan Zhou, 2022, AISTATS.

Object Discovery: Soft Attributed Graph Mining

Xuan Song, Quanshi Zhang, Ryosuke Shibasaki, 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

A fully online and unsupervised system for large and high-density area surveillance: Tracking, semantic scene learning and abnormality detection

H. Zha, R. Shibasaki, J. Cui, 2013, TIST.

Laser-based intelligent surveillance and abnormality detection in extremely crowded scenarios

Xuan Song, Quanshi Zhang, Ryosuke Shibasaki, 2012, 2012 IEEE International Conference on Robotics and Automation.

Interpreting CNN knowledge via an Explanatory Graph

Song-Chun Zhu, Y. Wu, Quanshi Zhang, 2017, AAAI.

Unsupervised Learning of Neural Networks to Explain Neural Networks pdf

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

Attributed Graph Mining and Matching: An Attempt to Define and Extract Soft Attributed Patterns

Xuan Song, Quanshi Zhang, Ryosuke Shibasaki, 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

A Simulator of Human Emergency Mobility Following Disasters: Knowledge Transfer from Big Disaster Data

Nicholas Jing Yuan, Xing Xie, R. Shibasaki, 2015, AAAI.

Start from minimum labeling: Learning of 3D object models and point labeling from a large and complex environment

Xuan Song, Quanshi Zhang, Ryosuke Shibasaki, 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

Unsupervised 3D category discovery and point labeling from a large urban environment

Xuan Song, Quanshi Zhang, Ryosuke Shibasaki, 2013, 2013 IEEE International Conference on Robotics and Automation.

Explanatory Graphs for CNNs pdf

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

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

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

Can We Faithfully Represent Absence States to Compute Shapley Values on a DNN?

Quanshi Zhang, J. Ren, Qirui Chen, 2023, ICLR.

Knowledge Consistency between Neural Networks and Beyond

Longfei Li, Quanshi Zhang, J. Wang, 2019, ICLR.