Moving object classification using horizontal laser scan data. - Huijing Zhao; Quanshi Zhang; Masaki Chiba; Ryosuke Shibasaki; Jinshi Cui; Hongbin Zha (ICRA, 2009)

Laser-based intelligent surveillance and abnormality detection in extremely crowded scenarios. - Xuan Song 001; Xiaowei Shao; Quanshi Zhang; Ryosuke Shibasaki; Huijing Zhao; Hongbin Zha (ICRA, 2012)

Category Modeling from Just a Single Labeling: Use Depth Information to Guide the Learning of 2D Models. - Quanshi Zhang; Xuan Song 001; Xiaowei Shao; Ryosuke Shibasaki; Huijing Zhao (CVPR, 2013)

Learning Graph Matching: Oriented to Category Modeling from Cluttered Scenes. - Quanshi Zhang; Xuan Song 001; Xiaowei Shao; Huijing Zhao; Ryosuke Shibasaki (ICCV, 2013)

Unsupervised 3D category discovery and point labeling from a large urban environment. - Quanshi Zhang; Xuan Song 001; Xiaowei Shao; Huijing Zhao; Ryosuke Shibasaki (ICRA, 2013)

Modeling and probabilistic reasoning of population evacuation during large-scale disaster. - Xuan Song 001; Quanshi Zhang; Yoshihide Sekimoto; Teerayut Horanont; Satoshi Ueyama; Ryosuke Shibasaki (KDD, 2013)

Intelligent System for Human Behavior Analysis and Reasoning Following Large-Scale Disasters. - Xuan Song 001; Quanshi Zhang; Yoshihide Sekimoto; Teerayut Horanont; Satoshi Ueyama; Ryosuke Shibasaki (IEEE Intell. Syst., 2013)

Unsupervised skeleton extraction and motion capture from 3D deformable matching. - Quanshi Zhang; Xuan Song 001; Xiaowei Shao; Ryosuke Shibasaki; Huijing Zhao (Neurocomputing, 2013)

A novel dynamic model for multiple pedestrians tracking in extremely crowded scenarios. - Xuan Song 001; Xiaowei Shao; Quanshi Zhang; Ryosuke Shibasaki; Huijing Zhao; Hongbin Zha (Inf. Fusion, 2013)

A fully online and unsupervised system for large and high-density area surveillance: Tracking, semantic scene learning and abnormality detection. - Xuan Song 001; Xiaowei Shao; Quanshi Zhang; Ryosuke Shibasaki; Huijing Zhao; Jinshi Cui; Hongbin Zha (ACM Trans. Intell. Syst. Technol., 2013)

Intelligent System for Urban Emergency Management during Large-Scale Disaster. - Xuan Song 001; Quanshi Zhang; Yoshihide Sekimoto; Ryosuke Shibasaki (AAAI, 2014)

When 3D Reconstruction Meets Ubiquitous RGB-D Images. - Quanshi Zhang; Xuan Song 001; Xiaowei Shao; Huijing Zhao; Ryosuke Shibasaki (CVPR, 2014)

Attributed Graph Mining and Matching: An Attempt to Define and Extract Soft Attributed Patterns. - Quanshi Zhang; Xuan Song 001; Xiaowei Shao; Huijing Zhao; Ryosuke Shibasaki (CVPR, 2014)

Start from minimum labeling: Learning of 3D object models and point labeling from a large and complex environment. - Quanshi Zhang; Xuan Song 001; Xiaowei Shao; Huijing Zhao; Ryosuke Shibasaki (ICRA, 2014)

Prediction of human emergency behavior and their mobility following large-scale disaster. - Xuan Song 001; Quanshi Zhang; Yoshihide Sekimoto; Ryosuke Shibasaki (KDD, 2014)

A Simulator of Human Emergency Mobility Following Disasters: Knowledge Transfer from Big Disaster Data. - Xuan Song 001; Quanshi Zhang; Yoshihide Sekimoto; Ryosuke Shibasaki; Nicholas Jing Yuan; Xing Xie 001 (AAAI, 2015)

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

From RGB-D Images to RGB Images: Single Labeling for Mining Visual Models. - Quanshi Zhang; Xuan Song 001; Xiaowei Shao; Huijing Zhao; Ryosuke Shibasaki (ACM Trans. Intell. Syst. Technol., 2015)

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

Object Discovery: Soft Attributed Graph Mining. - Quanshi Zhang; Xuan Song 001; Xiaowei Shao; Huijing Zhao; Ryosuke Shibasaki (IEEE Trans. Pattern Anal. Mach. Intell., 2016)

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

Visual Graph Mining. - Quanshi Zhang; Xuan Song 001; Ryosuke Shibasaki (CoRR, 2017)

Prediction and Simulation of Human Mobility Following Natural Disasters. - Xuan Song 001; Quanshi Zhang; Yoshihide Sekimoto; Ryosuke Shibasaki; Nicholas Jing Yuan; Xing Xie 001 (ACM Trans. Intell. Syst. Technol., 2017)

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

Examining CNN Representations With Respect to Dataset Bias. - Quanshi Zhang; Wenguan Wang; Song-Chun Zhu (AAAI, 2018)

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

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)

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)

Visual interpretability for deep learning: a survey. - Quanshi Zhang; Song-Chun Zhu (Frontiers Inf. Technol. Electron. Eng., 2018)

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

Explaining Neural Networks Semantically and Quantitatively. - Runjin Chen; Hao Chen 099; Ge Huang; Jie Ren 018; Quanshi Zhang (ICCV, 2019)

Towards a Deep and Unified Understanding of Deep Neural Models in NLP. - Chaoyu Guan; Xiting Wang; Quanshi Zhang; Runjin Chen; Di He 001; Xing Xie 001 (ICML, 2019)

Reports of the Workshops Held at the 2019 AAAI Conference on Artificial Intelligence. - Guy Barash; Mauricio Castillo-Effen; Niyati Chhaya; Peter Clark; Huáscar Espinoza; Eitan Farchi; Christopher W. Geib; Odd Erik Gundersen; Seán Ó hÉigeartaigh; José Hernández-Orallo; Chiori Hori; Xiaowei Huang 001; et al. (AI Mag., 2019)

Explaining AlphaGo: Interpreting Contextual Effects in Neural Networks. - Zenan Ling; Haotian Ma; Yu Yang 007; Robert C. Qiu; Song-Chun Zhu; Quanshi Zhang (CoRR, 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)

Proceedings of AAAI 2019 Workshop on Network Interpretability for Deep Learning. - Quanshi Zhang; Lixin Fan; Bolei Zhou (CoRR, 2019)

Complex-Valued Neural Networks for Privacy Protection. - Liyao Xiang; Haotian Ma; Hao Zhang 063; Yifan Zhang; Quanshi Zhang (CoRR, 2019)

Quantifying Layerwise Information Discarding of Neural Networks. - Haotian Ma; Yinqing Zhang; Fan Zhou; Quanshi Zhang (CoRR, 2019)

Knowledge Isomorphism between Neural Networks. - Ruofan Liang; Tianlin Li; Longfei Li; Quanshi Zhang (CoRR, 2019)

Towards a Unified Evaluation of Explanation Methods without Ground Truth. - Hao Zhang 063; Jiayi Chen; Haotian Xue; Quanshi Zhang (CoRR, 2019)

Utility Analysis of Network Architectures for 3D Point Cloud Processing. - Shikun Huang; Binbin Zhang; Wen Shen 002; Zhihua Wei 001; Quanshi Zhang (CoRR, 2019)

Preventing Information Leakage with Neural Architecture Search. - Shuang Zhang 007; Liyao Xiang; Congcong Li; Yixuan Wang; Zeyu Liu; Quanshi Zhang; Bo Li 001 (CoRR, 2019)

Visual graph mining for graph matching. - Quanshi Zhang; Xuan Song 001; Yu Yang 007; Haotian Ma; Ryosuke Shibasaki (Comput. Vis. Image Underst., 2019)

Explaining Knowledge Distillation by Quantifying the Knowledge. - Xu Cheng 005; Zhefan Rao; Yilan Chen 002; Quanshi Zhang (CVPR, 2020)

3D-Rotation-Equivariant Quaternion Neural Networks. - Wen Shen 002; Binbin Zhang; Shikun Huang; Zhihua Wei 001; Quanshi Zhang (ECCV (20), 2020)

Knowledge Consistency between Neural Networks and Beyond. - Ruofan Liang; Tianlin Li; Longfei Li; Jing Wang; Quanshi Zhang (ICLR, 2020)

Interpretable Complex-Valued Neural Networks for Privacy Protection. - Liyao Xiang; Hao Zhang 063; Haotian Ma; Yifan Zhang; Jie Ren 018; Quanshi Zhang (ICLR, 2020)

Deep Quaternion Features for Privacy Protection. - Hao Zhang 063; Yiting Chen 003; Liyao Xiang; Haotian Ma; Jie Shi; Quanshi Zhang (CoRR, 2020)

Rotation-Equivariant Neural Networks for Privacy Protection. - Hao Zhang 063; Yiting Chen 003; Haotian Ma; Xu Cheng 005; Qihan Ren; Liyao Xiang; Jie Shi; Quanshi Zhang (CoRR, 2020)

Interpreting Hierarchical Linguistic Interactions in DNNs. - Die Zhang; Huilin Zhou; Xiaoyi Bao; Da Huo 002; Ruizhao Chen; Xu Cheng 005; Hao Zhang 063; Mengyue Wu; Quanshi Zhang (CoRR, 2020)

Achieving Adversarial Robustness via Sparsity. - Shufan Wang; Ningyi Liao; Liyao Xiang; Nanyang Ye 001; Quanshi Zhang (CoRR, 2020)

Interpreting Multivariate Interactions in DNNs. - Hao Zhang 063; Yichen Xie 002; Longjie Zheng; Die Zhang; Quanshi Zhang (CoRR, 2020)

Game-Theoretic Interactions of Different Orders. - Hao Zhang 063; Xu Cheng 005; Yiting Chen 003; Quanshi Zhang (CoRR, 2020)

Interpreting Multivariate Shapley Interactions in DNNs. - Hao Zhang 063; Yichen Xie 002; Longjie Zheng; Die Zhang; Quanshi Zhang (AAAI, 2021)

Building Interpretable Interaction Trees for Deep NLP Models. - Die Zhang; Hao Zhang 063; Huilin Zhou; Xiaoyi Bao; Da Huo 002; Ruizhao Chen; Xu Cheng 005; Mengyue Wu; Quanshi Zhang (AAAI, 2021)

Verifiability and Predictability: Interpreting Utilities of Network Architectures for Point Cloud Processing. - Wen Shen 002; Zhihua Wei 001; Shikun Huang; Binbin Zhang; Panyue Chen; Ping Zhao; Quanshi Zhang (CVPR, 2021)

Interpreting Attributions and Interactions of Adversarial Attacks. - Xin Wang 108; Shuyun Lin; Hao Zhang 063; Yufei Zhu; Quanshi Zhang (ICCV, 2021)

A Unified Approach to Interpreting and Boosting Adversarial Transferability. - Xin Wang 108; Jie Ren 018; Shuyun Lin; Xiangming Zhu 002; Yisen Wang 001; Quanshi Zhang (ICLR, 2021)

Interpreting and Boosting Dropout from a Game-Theoretic View. - Hao Zhang 063; Sen Li; Yinchao Ma; Mingjie Li; Yichen Xie 002; Quanshi Zhang (ICLR, 2021)

Interpreting and Disentangling Feature Components of Various Complexity from DNNs. - Jie Ren 018; Mingjie Li; Zexu Liu; Quanshi Zhang (ICML, 2021)

Interpretable Compositional Convolutional Neural Networks. - Wen Shen 002; Zhihua Wei 001; Shikun Huang; Binbin Zhang; Jiaqi Fan; Ping Zhao; Quanshi Zhang (IJCAI, 2021)

Visualizing the Emergence of Intermediate Visual Patterns in DNNs. - Mingjie Li; Shaobo Wang 001; Quanshi Zhang (NeurIPS, 2021)

Towards a Unified Game-Theoretic View of Adversarial Perturbations and Robustness. - Jie Ren 018; Die Zhang; Yisen Wang 001; Lu Chen; Zhanpeng Zhou; Yiting Chen 003; Xu Cheng 005; Xin Wang 108; Meng Zhou; Jie Shi; Quanshi Zhang (NeurIPS, 2021)

Interpreting Representation Quality of DNNs for 3D Point Cloud Processing. - Wen Shen 002; Qihan Ren; Dongrui Liu; Quanshi Zhang (NeurIPS, 2021)

Game-theoretic Understanding of Adversarially Learned Features. - Jie Ren 018; Die Zhang; Yisen Wang 001; Lu Chen; Zhanpeng Zhou; Xu Cheng 005; Xin Wang 108; Yiting Chen 003; Jie Shi; Quanshi Zhang (CoRR, 2021)

Learning Baseline Values for Shapley Values. - Jie Ren 018; Zhanpeng Zhou; Qirui Chen; Quanshi Zhang (CoRR, 2021)

A Game-Theoretic Taxonomy of Visual Concepts in DNNs. - Xu Cheng 005; Chuntung Chu; Yi Zheng; Jie Ren 018; Quanshi Zhang (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)

A Hypothesis for the Aesthetic Appreciation in Neural Networks. - Xu Cheng 005; Xin Wang 108; Haotian Xue; Zhengyang Liang; Quanshi Zhang (CoRR, 2021)

Towards Axiomatic, Hierarchical, and Symbolic Explanation for Deep Models. - Jie Ren 018; Mingjie Li; Qirui Chen; Huiqi Deng; Quanshi Zhang (CoRR, 2021)

Discovering and Explaining the Representation Bottleneck of DNNs. - Huiqi Deng; Qihan Ren; Xu Chen; Hao Zhang 063; Jie Ren 018; Quanshi Zhang (CoRR, 2021)

Trap of Feature Diversity in the Learning of MLPs. - Dongrui Liu; Shaobo Wang 001; Jie Ren 018; Kangrui Wang; Sheng Yin; Quanshi Zhang (CoRR, 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)

Interpretable Generative Adversarial Networks. - Chao Li 028; Kelu Yao; Jin Wang 039; Boyu Diao; Yongjun Xu 001; Quanshi Zhang (AAAI, 2022)

Exploring Image Regions Not Well Encoded by an INN. - Zenan Ling; Fan Zhou; Meng Wei; Quanshi Zhang (AISTATS, 2022)

RASAT: Integrating Relational Structures into Pretrained Seq2Seq Model for Text-to-SQL. - Jiexing Qi; Jingyao Tang; Ziwei He; Xiangpeng Wan; Yu Cheng 003; Chenghu Zhou; Xinbing Wang; Quanshi Zhang; Zhouhan Lin (EMNLP, 2022)

Discovering and Explaining the Representation Bottleneck of DNNS. - Huiqi Deng; Qihan Ren; Hao Zhang 063; Quanshi Zhang (ICLR, 2022)

Towards Theoretical Analysis of Transformation Complexity of ReLU DNNs. - Jie Ren 018; Mingjie Li; Meng Zhou; Shih-Han Chan; Quanshi Zhang (ICML, 2022)

Quantification and Analysis of Layer-wise and Pixel-wise Information Discarding. - Haotian Ma; Hao Zhang 063; Fan Zhou; Yinqing Zhang; Quanshi Zhang (ICML, 2022)

RASAT: Integrating Relational Structures into Pretrained Seq2Seq Model for Text-to-SQL. - Jiexing Qi; Jingyao Tang; Ziwei He; Xiangpeng Wan; Chenghu Zhou; Xinbing Wang; Quanshi Zhang; Zhouhan Lin (CoRR, 2022)

Why Adversarial Training of ReLU Networks Is Difficult? - Xu Cheng 005; Hao Zhang 063; Yue Xin; Wen Shen 002; Jie Ren 018; Quanshi Zhang (CoRR, 2022)

Batch Normalization Is Blind to the First and Second Derivatives of the Loss. - Zhanpeng Zhou; Wen Shen 002; Huixin Chen; Ling Tang 002; Quanshi Zhang (CoRR, 2022)

Proving Common Mechanisms Shared by Twelve Methods of Boosting Adversarial Transferability. - Quanshi Zhang; Xin Wang 108; Jie Ren 018; Xu Cheng 005; Shuyun Lin; Yisen Wang 001; Xiangming Zhu 002 (CoRR, 2022)

Rapid detection and recognition of whole brain activity in a freely behaving Caenorhabditis elegans. - Yuxiang Wu; Shang Wu; Xin Wang 108; Chengtian Lang; Quanshi Zhang; Quan Wen; Tianqi Xu (PLoS Comput. Biol., 2022)

Defining and Quantifying the Emergence of Sparse Concepts in DNNs. - Jie Ren 018; Mingjie Li; Qirui Chen; Huiqi Deng; Quanshi Zhang (CVPR, 2023)

Can We Faithfully Represent Absence States to Compute Shapley Values on a DNN? - Jie Ren 018; Zhanpeng Zhou; Qirui Chen; Quanshi Zhang (ICLR, 2023)

HarsanyiNet: Computing Accurate Shapley Values in a Single Forward Propagation. - Lu Chen; Siyu Lou; Keyan Zhang; Jin Huang; Quanshi Zhang (ICML, 2023)

Does a Neural Network Really Encode Symbolic Concepts? - Mingjie Li; Quanshi Zhang (ICML, 2023)

Bayesian Neural Networks Avoid Encoding Complex and Perturbation-Sensitive Concepts. - Qihan Ren; Huiqi Deng; Yunuo Chen 002; Siyu Lou; Quanshi Zhang (ICML, 2023)

Defects of Convolutional Decoder Networks in Frequency Representation. - Ling Tang 002; Wen Shen 002; Zhanpeng Zhou; Yuefeng Chen; Quanshi Zhang (ICML, 2023)

Towards the Difficulty for a Deep Neural Network to Learn Concepts of Different Complexities. - Dongrui Liu; Huiqi Deng; Xu Cheng 005; Qihan Ren; Kangrui Wang; Quanshi Zhang (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)

Does a Neural Network Really Encode Symbolic Concept? - Mingjie Li; Quanshi Zhang (CoRR, 2023)

Concept-Level Explanation for the Generalization of a DNN. - Huilin Zhou; Hao Zhang 063; Huiqi Deng; Dongrui Liu; Wen Shen 002; Shih-Han Chan; Quanshi Zhang (CoRR, 2023)

Bayesian Neural Networks Tend to Ignore Complex and Sensitive Concepts. - Qihan Ren; Huiqi Deng; Yunuo Chen 002; Siyu Lou; Quanshi Zhang (CoRR, 2023)

Understanding and Unifying Fourteen Attribution Methods with Taylor Interactions. - Huiqi Deng; Na Zou 001; Mengnan Du; Weifu Chen; Guocan Feng; Ziwei Yang; Zheyang Li; Quanshi Zhang (CoRR, 2023)

Can the Inference Logic of Large Language Models be Disentangled into Symbolic Concepts? - Wen Shen 002; Lei Cheng 006; Yuxiao Yang; Mingjie Li; Quanshi Zhang (CoRR, 2023)

Technical Note: Defining and Quantifying AND-OR Interactions for Faithful and Concise Explanation of DNNs. - Mingjie Li; Quanshi Zhang (CoRR, 2023)

Where We Have Arrived in Proving the Emergence of Sparse Symbolic Concepts in AI Models. - Qihan Ren; Jiayang Gao; Wen Shen 002; Quanshi Zhang (CoRR, 2023)

Towards Attributions of Input Variables in a Coalition. - Xinhao Zheng; Huiqi Deng; Quanshi Zhang (CoRR, 2023)

Explaining How a Neural Network Play the Go Game and Let People Learn. - Huilin Zhou; Huijie Tang; Mingjie Li; Hao Zhang 063; Zhenyu Liu; Quanshi Zhang (CoRR, 2023)

Quantifying the Knowledge in a DNN to Explain Knowledge Distillation for Classification. - Quanshi Zhang; Xu Cheng 005; Yilan Chen 002; Zhefan Rao (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Clarifying the Behavior and the Difficulty of Adversarial Training. - Xu Cheng 005; Hao Zhang 063; Yue Xin; Wen Shen 002; Quanshi Zhang (AAAI, 2024)

Batch Normalization Is Blind to the First and Second Derivatives of the Loss. - Zhanpeng Zhou; Wen Shen 002; Huixin Chen; Ling Tang 002; Yuefeng Chen; Quanshi Zhang (AAAI, 2024)

Explaining Generalization Power of a DNN Using Interactive Concepts. - Huilin Zhou; Hao Zhang 063; Huiqi Deng; Dongrui Liu; Wen Shen 002; Shih-Han Chan; Quanshi Zhang (AAAI, 2024)

Identifying Semantic Induction Heads to Understand In-Context Learning. - Jie Ren 018; Qipeng Guo; Hang Yan 001; Dongrui Liu; Quanshi Zhang; Xipeng Qiu; Dahua Lin (ACL (Findings), 2024)

Defining and extracting generalizable interaction primitives from DNNs. - Lu Chen; Siyu Lou; Benhao Huang; Quanshi Zhang (ICLR, 2024)

Where We Have Arrived in Proving the Emergence of Sparse Interaction Primitives in DNNs. - Qihan Ren; Jiayang Gao; Wen Shen 002; Quanshi Zhang (ICLR, 2024)

Layerwise Change of Knowledge in Neural Networks. - Xu Cheng 005; Lei Cheng 006; Zhaoran Peng; Yang Xu; Tian Han 001; Quanshi Zhang (ICML, 2024)

Towards the Dynamics of a DNN Learning Symbolic Interactions. - Qihan Ren; Junpeng Zhang; Yang Xu; Yue Xin; Dongrui Liu; Quanshi Zhang (NeurIPS, 2024)

Two-Phase Dynamics of Interactions Explains the Starting Point of a DNN Learning Over-Fitted Features. - Junpeng Zhang; Qing Li; Liang Lin; Quanshi Zhang (CoRR, 2024)

Quantifying In-Context Reasoning Effects and Memorization Effects in LLMs. - Siyu Lou; Yuntian Chen; Xiaodan Liang; Liang Lin; Quanshi Zhang (CoRR, 2024)

Disentangling Regional Primitives for Image Generation. - Zhengting Chen; Lei Cheng 006; Lianghui Ding; Quanshi Zhang (CoRR, 2024)

Alignment Between the Decision-Making Logic of LLMs and Human Cognition: A Case Study on Legal LLMs. - Lu Chen; Yuxuan Huang; Yixing Li; Yaohui Jin; Shuai Zhao; Zilong Zheng; Quanshi Zhang (CoRR, 2024)

Unifying Fourteen Post-Hoc Attribution Methods With Taylor Interactions. - Huiqi Deng; Na Zou 001; Mengnan Du; Weifu Chen; Guocan Feng; Ziwei Yang; Zheyang Li; Quanshi Zhang (IEEE Trans. Pattern Anal. Mach. Intell., 2024)

An Introspective Data Augmentation Method for Training Math Word Problem Solvers. - Jinghui Qin; Zhongzhan Huang; Ying Zeng; Quanshi Zhang; Liang Lin (IEEE ACM Trans. Audio Speech Lang. Process., 2024)

Interpretable Rotation-Equivariant Quaternion Neural Networks for 3D Point Cloud Processing. - Wen Shen 002; Zhihua Wei 001; Qihan Ren; Binbin Zhang; Shikun Huang; Jiaqi Fan; Quanshi Zhang (IEEE Trans. Pattern Anal. Mach. Intell., 2024)

Learning to Prevent Input Leakages in the Mobile Cloud Inference. - Liyao Xiang; Shuang Zhang 007; Quanshi Zhang (IEEE Trans. Mob. Comput., 2024)

Interpretability of Neural Networks Based on Game-theoretic Interactions. - Huilin Zhou; Jie Ren 018; Huiqi Deng; Xu Cheng 005; Jinpeng Zhang; Quanshi Zhang (Mach. Intell. Res., 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)

Randomness of Low-Layer Parameters Determines Confusing Samples in Terms of Interaction Representations of a DNN. - Junpeng Zhang; Lei Cheng 006; Qing Li; Liang Lin; Quanshi Zhang (CoRR, 2025)

Revisiting Generalization Power of a DNN in Terms of Symbolic Interactions. - Lei Cheng 006; Junpeng Zhang; Qihan Ren; Quanshi Zhang (CoRR, 2025)

Towards the Resistance of Neural Network Watermarking to Fine-tuning. - Ling Tang 002; Yuefeng Chen; Hui Xue; Quanshi Zhang (CoRR, 2025)

Technical Report: Quantifying and Analyzing the Generalization Power of a DNN. - Yuxuan He; Junpeng Zhang; Lei Cheng 006; Hongyuan Zhang; Quanshi Zhang (CoRR, 2025)

Towards the first principles of explaining DNNs: interactions explain the learning dynamics. - Huilin Zhou; Qihan Ren; Junpeng Zhang; Quanshi Zhang (Frontiers Inf. Technol. Electron. Eng., 2025)

Attribution Explanations for Deep Neural Networks: A Theoretical Perspective. - Huiqi Deng; Hongbin Pei; Quanshi Zhang; Mengnan Du (CoRR, 2025)