| 2026 | ACL | Challenging the Explanation Based on Preceding Tokens: Discovering Transferable Non-Literal Biasing. | Yuchen Huang, Junpeng Zhang, Quanshi Zhang |
| 2025 | AAAI | Monitoring Primitive Interactions During the Training of DNNs. | Jie Ren, Xinhao Zheng, Jiyu Liu, Andrew Lizarraga, Ying Nian Wu, Liang Lin, Quanshi Zhang |
| 2025 | ICML | Towards Attributions of Input Variables in a Coalition. | Xinhao Zheng, Huiqi Deng, Quanshi Zhang |
| 2024 | AAAI | Clarifying the Behavior and the Difficulty of Adversarial Training. | Xu Cheng, Hao Zhang, Yue Xin, Wen Shen, Quanshi Zhang |
| 2024 | AAAI | Batch Normalization Is Blind to the First and Second Derivatives of the Loss. | Zhanpeng Zhou, Wen Shen, Huixin Chen, Ling Tang, Yuefeng Chen, Quanshi Zhang |
| 2024 | AAAI | Explaining Generalization Power of a DNN Using Interactive Concepts. | Huilin Zhou, Hao Zhang, Huiqi Deng, Dongrui Liu, Wen Shen, Shih-Han Chan, Quanshi Zhang |
| 2024 | ACL | Identifying Semantic Induction Heads to Understand In-Context Learning. | Jie Ren, Qipeng Guo, Hang Yan, Dongrui Liu, Quanshi Zhang, Xipeng Qiu, Dahua Lin |
| 2024 | ICLR | Defining and extracting generalizable interaction primitives from DNNs. | Lu Chen, Siyu Lou, Benhao Huang, Quanshi Zhang |
| 2024 | ICLR | Where We Have Arrived in Proving the Emergence of Sparse Interaction Primitives in DNNs. | Qihan Ren, Jiayang Gao, Wen Shen, Quanshi Zhang |
| 2024 | ICML | Layerwise Change of Knowledge in Neural Networks. | Xu Cheng, Lei Cheng, Zhaoran Peng, Yang Xu, Tian Han, Quanshi Zhang |
| 2023 | CVPR | Defining and Quantifying the Emergence of Sparse Concepts in DNNs. | Jie Ren, Mingjie Li, Qirui Chen, Huiqi Deng, Quanshi Zhang |
| 2023 | ICLR | Can We Faithfully Represent Absence States to Compute Shapley Values on a DNN? | Jie Ren, Zhanpeng Zhou, Qirui Chen, Quanshi Zhang |
| 2023 | ICML | HarsanyiNet: Computing Accurate Shapley Values in a Single Forward Propagation. | Lu Chen, Siyu Lou, Keyan Zhang, Jin Huang, Quanshi Zhang |
| 2023 | ICML | Does a Neural Network Really Encode Symbolic Concepts? | Mingjie Li, Quanshi Zhang |
| 2023 | ICML | Bayesian Neural Networks Avoid Encoding Complex and Perturbation-Sensitive Concepts. | Qihan Ren, Huiqi Deng, Yunuo Chen, Siyu Lou, Quanshi Zhang |
| 2023 | ICML | Defects of Convolutional Decoder Networks in Frequency Representation. | Ling Tang, Wen Shen, Zhanpeng Zhou, Yuefeng Chen, Quanshi Zhang |
| 2022 | AAAI | Interpretable Generative Adversarial Networks. | Chao Li, Kelu Yao, Jin Wang, Boyu Diao, Yongjun Xu, Quanshi Zhang |
| 2022 | AISTATS | Exploring Image Regions Not Well Encoded by an INN. | Zenan Ling, Fan Zhou, Meng Wei, Quanshi Zhang |
| 2022 | EMNLP | RASAT: Integrating Relational Structures into Pretrained Seq2Seq Model for Text-to-SQL. | Jiexing Qi, Jingyao Tang, Ziwei He, Xiangpeng Wan, Yu Cheng, Chenghu Zhou, Xinbing Wang, Quanshi Zhang, Zhouhan Lin |
| 2022 | ICLR | Discovering and Explaining the Representation Bottleneck of DNNS. | Huiqi Deng, Qihan Ren, Hao Zhang, Quanshi Zhang |
| 2022 | ICML | Towards Theoretical Analysis of Transformation Complexity of ReLU DNNs. | Jie Ren, Mingjie Li, Meng Zhou, Shih-Han Chan, Quanshi Zhang |
| 2022 | ICML | Quantification and Analysis of Layer-wise and Pixel-wise Information Discarding. | Haotian Ma, Hao Zhang, Fan Zhou, Yinqing Zhang, Quanshi Zhang |
| 2021 | AAAI | Interpreting Multivariate Shapley Interactions in DNNs. | Hao Zhang, Yichen Xie, Longjie Zheng, Die Zhang, Quanshi Zhang |
| 2021 | AAAI | Building Interpretable Interaction Trees for Deep NLP Models. | Die Zhang, Hao Zhang, Huilin Zhou, Xiaoyi Bao, Da Huo, Ruizhao Chen, Xu Cheng, Mengyue Wu, Quanshi Zhang |
| 2021 | CVPR | Verifiability and Predictability: Interpreting Utilities of Network Architectures for Point Cloud Processing. | Wen Shen, Zhihua Wei, Shikun Huang, Binbin Zhang, Panyue Chen, Ping Zhao, Quanshi Zhang |
| 2021 | ICCV | Interpreting Attributions and Interactions of Adversarial Attacks. | Xin Wang, Shuyun Lin, Hao Zhang, Yufei Zhu, Quanshi Zhang |
| 2021 | ICLR | A Unified Approach to Interpreting and Boosting Adversarial Transferability. | Xin Wang, Jie Ren, Shuyun Lin, Xiangming Zhu, Yisen Wang, Quanshi Zhang |
| 2021 | ICLR | Interpreting and Boosting Dropout from a Game-Theoretic View. | Hao Zhang, Sen Li, Yinchao Ma, Mingjie Li, Yichen Xie, Quanshi Zhang |
| 2021 | ICML | Interpreting and Disentangling Feature Components of Various Complexity from DNNs. | Jie Ren, Mingjie Li, Zexu Liu, Quanshi Zhang |
| 2021 | IJCAI | Interpretable Compositional Convolutional Neural Networks. | Wen Shen, Zhihua Wei, Shikun Huang, Binbin Zhang, Jiaqi Fan, Ping Zhao, Quanshi Zhang |
| 2020 | CVPR | Explaining Knowledge Distillation by Quantifying the Knowledge. | Xu Cheng, Zhefan Rao, Yilan Chen, Quanshi Zhang |
| 2020 | ECCV | 3D-Rotation-Equivariant Quaternion Neural Networks. | Wen Shen, Binbin Zhang, Shikun Huang, Zhihua Wei, Quanshi Zhang |
| 2020 | ICLR | Knowledge Consistency between Neural Networks and Beyond. | Ruofan Liang, Tianlin Li, Longfei Li, Jing Wang, Quanshi Zhang |
| 2020 | ICLR | Interpretable Complex-Valued Neural Networks for Privacy Protection. | Liyao Xiang, Hao Zhang, Haotian Ma, Yifan Zhang, Jie Ren, Quanshi Zhang |
| 2019 | CVPR | Interpreting CNNs via Decision Trees. | Quanshi Zhang, Yu Yang, Haotian Ma, Ying Nian Wu |
| 2019 | ICCV | Explaining Neural Networks Semantically and Quantitatively. | Runjin Chen, Hao Chen, Ge Huang, Jie Ren, Quanshi Zhang |
| 2019 | ICML | Towards a Deep and Unified Understanding of Deep Neural Models in NLP. | Chaoyu Guan, Xiting Wang, Quanshi Zhang, Runjin Chen, Di He, Xing Xie |
| 2018 | AAAI | Interpreting CNN Knowledge via an Explanatory Graph. | Quanshi Zhang, Ruiming Cao, Feng Shi, Ying Nian Wu, Song-Chun Zhu |
| 2018 | AAAI | Examining CNN Representations With Respect to Dataset Bias. | Quanshi Zhang, Wenguan Wang, Song-Chun Zhu |
| 2018 | CVPR | Interpretable Convolutional Neural Networks. | Quanshi Zhang, Ying Nian Wu, Song-Chun Zhu |
| 2017 | AAAI | Growing Interpretable Part Graphs on ConvNets via Multi-Shot Learning. | Quanshi Zhang, Ruiming Cao, Ying Nian Wu, Song-Chun Zhu |
| 2017 | CVPR | Mining Object Parts from CNNs via Active Question-Answering. | Quanshi Zhang, Ruiming Cao, Ying Nian Wu, Song-Chun Zhu |
| 2015 | AAAI | A Simulator of Human Emergency Mobility Following Disasters: Knowledge Transfer from Big Disaster Data. | Xuan Song, Quanshi Zhang, Yoshihide Sekimoto, Ryosuke Shibasaki, Nicholas Jing Yuan, Xing Xie |
| 2015 | ICCV | Mining And-Or Graphs for Graph Matching and Object Discovery. | Quanshi Zhang, Ying Nian Wu, Song-Chun Zhu |
| 2014 | AAAI | Intelligent System for Urban Emergency Management during Large-Scale Disaster. | Xuan Song, Quanshi Zhang, Yoshihide Sekimoto, Ryosuke Shibasaki |
| 2014 | CVPR | When 3D Reconstruction Meets Ubiquitous RGB-D Images. | Quanshi Zhang, Xuan Song, Xiaowei Shao, Huijing Zhao, Ryosuke Shibasaki |
| 2014 | CVPR | Attributed Graph Mining and Matching: An Attempt to Define and Extract Soft Attributed Patterns. | Quanshi Zhang, Xuan Song, Xiaowei Shao, Huijing Zhao, Ryosuke Shibasaki |
| 2014 | ICRA | Start from minimum labeling: Learning of 3D object models and point labeling from a large and complex environment. | Quanshi Zhang, Xuan Song, Xiaowei Shao, Huijing Zhao, Ryosuke Shibasaki |
| 2014 | KDD | Prediction of human emergency behavior and their mobility following large-scale disaster. | Xuan Song, Quanshi Zhang, Yoshihide Sekimoto, Ryosuke Shibasaki |
| 2013 | CVPR | Category Modeling from Just a Single Labeling: Use Depth Information to Guide the Learning of 2D Models. | Quanshi Zhang, Xuan Song, Xiaowei Shao, Ryosuke Shibasaki, Huijing Zhao |
| 2013 | ICCV | Learning Graph Matching: Oriented to Category Modeling from Cluttered Scenes. | Quanshi Zhang, Xuan Song, Xiaowei Shao, Huijing Zhao, Ryosuke Shibasaki |
| 2013 | ICRA | Unsupervised 3D category discovery and point labeling from a large urban environment. | Quanshi Zhang, Xuan Song, Xiaowei Shao, Huijing Zhao, Ryosuke Shibasaki |
| 2013 | KDD | Modeling and probabilistic reasoning of population evacuation during large-scale disaster. | Xuan Song, Quanshi Zhang, Yoshihide Sekimoto, Teerayut Horanont, Satoshi Ueyama, Ryosuke Shibasaki |
| 2012 | ICRA | Laser-based intelligent surveillance and abnormality detection in extremely crowded scenarios. | Xuan Song, Xiaowei Shao, Quanshi Zhang, Ryosuke Shibasaki, Huijing Zhao, Hongbin Zha |
| 2009 | ICRA | Moving object classification using horizontal laser scan data. | Huijing Zhao, Quanshi Zhang, Masaki Chiba, Ryosuke Shibasaki, Jinshi Cui, Hongbin Zha |