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2020 – today
- 2024
[j21]Huilin Zhou
, Jie Ren, Huiqi Deng, Xu Cheng, Jinpeng Zhang, Quanshi Zhang:
Interpretability of Neural Networks Based on Game-theoretic Interactions. Mach. Intell. Res. 21(4): 718-739 (2024)- [j20]Wen Shen, Zhihua Wei, Qihan Ren, Binbin Zhang, Shikun Huang, Jiaqi Fan, Quanshi Zhang:
Interpretable Rotation-Equivariant Quaternion Neural Networks for 3D Point Cloud Processing. IEEE Trans. Pattern Anal. Mach. Intell. 46(5): 3290-3304 (2024) - [j19]Huiqi Deng, Na Zou, Mengnan Du, Weifu Chen, Guocan Feng, Ziwei Yang, Zheyang Li, Quanshi Zhang:
Unifying Fourteen Post-Hoc Attribution Methods With Taylor Interactions. IEEE Trans. Pattern Anal. Mach. Intell. 46(7): 4625-4640 (2024) - [j18]Jinghui Qin, Zhongzhan Huang, Ying Zeng, Quanshi Zhang, Liang Lin:
An Introspective Data Augmentation Method for Training Math Word Problem Solvers. IEEE ACM Trans. Audio Speech Lang. Process. 32: 3113-3127 (2024) - [j17]Liyao Xiang, Shuang Zhang, Quanshi Zhang:
Learning to Prevent Input Leakages in the Mobile Cloud Inference. IEEE Trans. Mob. Comput. 23(7): 7650-7663 (2024) - [c57]Xu Cheng, Hao Zhang, Yue Xin, Wen Shen, Quanshi Zhang:
Clarifying the Behavior and the Difficulty of Adversarial Training. AAAI 2024: 11507-11515 - [c56]Huilin Zhou, Hao Zhang, Huiqi Deng, Dongrui Liu, Wen Shen, Shih-Han Chan, Quanshi Zhang:
Explaining Generalization Power of a DNN Using Interactive Concepts. AAAI 2024: 17105-17113 - [c55]Zhanpeng Zhou, Wen Shen, Huixin Chen, Ling Tang, Yuefeng Chen, Quanshi Zhang:
Batch Normalization Is Blind to the First and Second Derivatives of the Loss. AAAI 2024: 20010-20018 - [c54]Jie Ren, Qipeng Guo, Hang Yan, Dongrui Liu, Quanshi Zhang, Xipeng Qiu, Dahua Lin:
Identifying Semantic Induction Heads to Understand In-Context Learning. ACL (Findings) 2024: 6916-6932 - [c53]Lu Chen, Siyu Lou, Benhao Huang, Quanshi Zhang:
Defining and extracting generalizable interaction primitives from DNNs. ICLR 2024 - [c52]Qihan Ren, Jiayang Gao, Wen Shen, Quanshi Zhang:
Where We Have Arrived in Proving the Emergence of Sparse Interaction Primitives in DNNs. ICLR 2024 - [c51]Xu Cheng, Lei Cheng, Zhaoran Peng, Yang Xu, Tian Han, Quanshi Zhang:
Layerwise Change of Knowledge in Neural Networks. ICML 2024 - [i73]Lu Chen, Siyu Lou, Benhao Huang, Quanshi Zhang:
Defining and Extracting generalizable interaction primitives from DNNs. CoRR abs/2401.16318 (2024) - [i72]Junpeng Zhang, Qing Li, Liang Lin, Quanshi Zhang:
Two-Phase Dynamics of Interactions Explains the Starting Point of a DNN Learning Over-Fitted Features. CoRR abs/2405.10262 (2024) - [i71]Siyu Lou, Yuntian Chen, Xiaodan Liang, Liang Lin, Quanshi Zhang:
Quantifying In-Context Reasoning Effects and Memorization Effects in LLMs. CoRR abs/2405.11880 (2024) - [i70]Qihan Ren, Yang Xu, Junpeng Zhang, Yue Xin, Dongrui Liu, Quanshi Zhang:
Towards the Dynamics of a DNN Learning Symbolic Interactions. CoRR abs/2407.19198 (2024) - [i69]Xu Cheng, Lei Cheng, Zhaoran Peng, Yang Xu, Tian Han, Quanshi Zhang:
Layerwise Change of Knowledge in Neural Networks. CoRR abs/2409.08712 (2024) - [i68]Zhengting Chen, Lei Cheng, Lianghui Ding, Quanshi Zhang:
Disentangling Regional Primitives for Image Generation. CoRR abs/2410.04421 (2024) - 2023
- [j16]Quanshi Zhang, Xu Cheng, Yilan Chen, Zhefan Rao:
Quantifying the Knowledge in a DNN to Explain Knowledge Distillation for Classification. IEEE Trans. Pattern Anal. Mach. Intell. 45(4): 5099-5113 (2023) - [c50]Jie Ren, Mingjie Li, Qirui Chen, Huiqi Deng, Quanshi Zhang:
Defining and Quantifying the Emergence of Sparse Concepts in DNNs. CVPR 2023: 20280-20289 - [c49]Jie Ren, Zhanpeng Zhou, Qirui Chen, Quanshi Zhang:
Can We Faithfully Represent Absence States to Compute Shapley Values on a DNN? ICLR 2023 - [c48]Lu Chen, Siyu Lou, Keyan Zhang, Jin Huang, Quanshi Zhang:
HarsanyiNet: Computing Accurate Shapley Values in a Single Forward Propagation. ICML 2023: 4804-4825 - [c47]Mingjie Li, Quanshi Zhang:
Does a Neural Network Really Encode Symbolic Concepts? ICML 2023: 20452-20469 - [c46]Qihan Ren, Huiqi Deng, Yunuo Chen, Siyu Lou, Quanshi Zhang:
Bayesian Neural Networks Avoid Encoding Complex and Perturbation-Sensitive Concepts. ICML 2023: 28889-28913 - [c45]Ling Tang, Wen Shen, Zhanpeng Zhou, Yuefeng Chen, Quanshi Zhang:
Defects of Convolutional Decoder Networks in Frequency Representation. ICML 2023: 33758-33791 - [c44]Dongrui Liu, Huiqi Deng, Xu Cheng, Qihan Ren, Kangrui Wang, Quanshi Zhang:
Towards the Difficulty for a Deep Neural Network to Learn Concepts of Different Complexities. NeurIPS 2023 - [c43]Quanshi Zhang, Xu Cheng, Xin Wang, Yu Yang, Yingnian Wu:
Network Transplanting for the Functionally Modular Architecture. PRCV (3) 2023: 69-83 - [i67]Mingjie Li, Quanshi Zhang:
Does a Neural Network Really Encode Symbolic Concept? CoRR abs/2302.13080 (2023) - [i66]Huilin Zhou, Hao Zhang, Huiqi Deng, Dongrui Liu, Wen Shen, Shih-Han Chan, Quanshi Zhang:
Concept-Level Explanation for the Generalization of a DNN. CoRR abs/2302.13091 (2023) - [i65]Qihan Ren, Huiqi Deng, Yunuo Chen, Siyu Lou, Quanshi Zhang:
Bayesian Neural Networks Tend to Ignore Complex and Sensitive Concepts. CoRR abs/2302.13095 (2023) - [i64]Huiqi Deng, Na Zou, Mengnan Du, Weifu Chen, Guocan Feng, Ziwei Yang, Zheyang Li, Quanshi Zhang:
Understanding and Unifying Fourteen Attribution Methods with Taylor Interactions. CoRR abs/2303.01506 (2023) - [i63]Wen Shen, Lei Cheng, Yuxiao Yang, Mingjie Li, Quanshi Zhang:
Can the Inference Logic of Large Language Models be Disentangled into Symbolic Concepts? CoRR abs/2304.01083 (2023) - [i62]Lu Chen, Siyu Lou, Keyan Zhang, Jin Huang, Quanshi Zhang:
HarsanyiNet: Computing Accurate Shapley Values in a Single Forward Propagation. CoRR abs/2304.01811 (2023) - [i61]Mingjie Li, Quanshi Zhang:
Technical Note: Defining and Quantifying AND-OR Interactions for Faithful and Concise Explanation of DNNs. CoRR abs/2304.13312 (2023) - [i60]Qihan Ren, Jiayang Gao, Wen Shen, Quanshi Zhang:
Where We Have Arrived in Proving the Emergence of Sparse Symbolic Concepts in AI Models. CoRR abs/2305.01939 (2023) - [i59]Xinhao Zheng, Huiqi Deng, Quanshi Zhang:
Towards Attributions of Input Variables in a Coalition. CoRR abs/2309.13411 (2023) - [i58]Huilin Zhou, Huijie Tang, Mingjie Li, Hao Zhang, Zhenyu Liu, Quanshi Zhang:
Explaining How a Neural Network Play the Go Game and Let People Learn. CoRR abs/2310.09838 (2023) - 2022
- [j15]Yuxiang Wu, Shang Wu, Xin Wang, Chengtian Lang, Quanshi Zhang, Quan Wen, Tianqi Xu:
Rapid detection and recognition of whole brain activity in a freely behaving Caenorhabditis elegans. PLoS Comput. Biol. 18(10): 1010594 (2022) - [c42]Chao Li, Kelu Yao, Jin Wang, Boyu Diao, Yongjun Xu, Quanshi Zhang:
Interpretable Generative Adversarial Networks. AAAI 2022: 1280-1288 - [c41]Zenan Ling, Fan Zhou, Meng Wei, Quanshi Zhang:
Exploring Image Regions Not Well Encoded by an INN. AISTATS 2022: 483-509 - [c40]Jiexing Qi, Jingyao Tang, Ziwei He, Xiangpeng Wan, Yu Cheng, Chenghu Zhou, Xinbing Wang, Quanshi Zhang, Zhouhan Lin:
RASAT: Integrating Relational Structures into Pretrained Seq2Seq Model for Text-to-SQL. EMNLP 2022: 3215-3229 - [c39]Huiqi Deng, Qihan Ren, Hao Zhang, Quanshi Zhang:
Discovering and Explaining the Representation Bottleneck of DNNS. ICLR 2022 - [c38]Haotian Ma, Hao Zhang, Fan Zhou, Yinqing Zhang, Quanshi Zhang:
Quantification and Analysis of Layer-wise and Pixel-wise Information Discarding. ICML 2022: 14664-14698 - [c37]Jie Ren, Mingjie Li, Meng Zhou, Shih-Han Chan, Quanshi Zhang:
Towards Theoretical Analysis of Transformation Complexity of ReLU DNNs. ICML 2022: 18537-18558 - [i57]Jie Ren, Mingjie Li, Meng Zhou, Shih-Han Chan, Quanshi Zhang:
Towards Theoretical Analysis of Transformation Complexity of ReLU DNNs. CoRR abs/2205.01940 (2022) - [i56]Jiexing Qi, Jingyao Tang, Ziwei He, Xiangpeng Wan, Chenghu Zhou, Xinbing Wang, Quanshi Zhang, Zhouhan Lin:
RASAT: Integrating Relational Structures into Pretrained Seq2Seq Model for Text-to-SQL. CoRR abs/2205.06983 (2022) - [i55]Xu Cheng, Hao Zhang, Yue Xin, Wen Shen, Jie Ren, Quanshi Zhang:
Why Adversarial Training of ReLU Networks Is Difficult? CoRR abs/2205.15130 (2022) - [i54]Zhanpeng Zhou, Wen Shen, Huixin Chen, Ling Tang, Quanshi Zhang:
Batch Normalization Is Blind to the First and Second Derivatives of the Loss. CoRR abs/2205.15146 (2022) - [i53]Quanshi Zhang, Xin Wang, Jie Ren, Xu Cheng, Shuyun Lin, Yisen Wang, Xiangming Zhu:
Proving Common Mechanisms Shared by Twelve Methods of Boosting Adversarial Transferability. CoRR abs/2207.11694 (2022) - [i52]Quanshi Zhang, Xu Cheng, Yilan Chen, Zhefan Rao:
Quantifying the Knowledge in a DNN to Explain Knowledge Distillation for Classification. CoRR abs/2208.08741 (2022) - [i51]Ling Tang, Wen Shen, Zhanpeng Zhou, Yuefeng Chen, Quanshi Zhang:
Defects of Convolutional Decoder Networks in Frequency Representation. CoRR abs/2210.09020 (2022) - 2021
- [j14]Quanshi Zhang, Xin Wang, Ying Nian Wu, Huilin Zhou, Song-Chun Zhu:
Interpretable CNNs for Object Classification. IEEE Trans. Pattern Anal. Mach. Intell. 43(10): 3416-3431 (2021) - [j13]Quanshi Zhang, Xin Wang, Ruiming Cao, Ying Nian Wu, Feng Shi, Song-Chun Zhu:
Extraction of an Explanatory Graph to Interpret a CNN. IEEE Trans. Pattern Anal. Mach. Intell. 43(11): 3863-3877 (2021) - [j12]Quanshi Zhang, Jie Ren, Ge Huang, Ruiming Cao, Ying Nian Wu, Song-Chun Zhu:
Mining Interpretable AOG Representations From Convolutional Networks via Active Question Answering. IEEE Trans. Pattern Anal. Mach. Intell. 43(11): 3949-3963 (2021) - [c36]Hao Zhang, Yichen Xie, Longjie Zheng, Die Zhang, Quanshi Zhang:
Interpreting Multivariate Shapley Interactions in DNNs. AAAI 2021: 10877-10886 - [c35]Die Zhang, Hao Zhang, Huilin Zhou, Xiaoyi Bao, Da Huo, Ruizhao Chen, Xu Cheng, Mengyue Wu, Quanshi Zhang:
Building Interpretable Interaction Trees for Deep NLP Models. AAAI 2021: 14328-14337 - [c34]Wen Shen, Zhihua Wei, Shikun Huang, Binbin Zhang, Panyue Chen, Ping Zhao, Quanshi Zhang:
Verifiability and Predictability: Interpreting Utilities of Network Architectures for Point Cloud Processing. CVPR 2021: 10703-10712 - [c33]Xin Wang, Shuyun Lin, Hao Zhang, Yufei Zhu, Quanshi Zhang:
Interpreting Attributions and Interactions of Adversarial Attacks. ICCV 2021: 1075-1084 - [c32]Xin Wang, Jie Ren, Shuyun Lin, Xiangming Zhu, Yisen Wang, Quanshi Zhang:
A Unified Approach to Interpreting and Boosting Adversarial Transferability. ICLR 2021 - [c31]Hao Zhang, Sen Li, Yinchao Ma, Mingjie Li, Yichen Xie, Quanshi Zhang:
Interpreting and Boosting Dropout from a Game-Theoretic View. ICLR 2021 - [c30]Jie Ren, Mingjie Li, Zexu Liu, Quanshi Zhang:
Interpreting and Disentangling Feature Components of Various Complexity from DNNs. ICML 2021: 8971-8981 - [c29]Wen Shen, Zhihua Wei, Shikun Huang, Binbin Zhang, Jiaqi Fan, Ping Zhao, Quanshi Zhang:
Interpretable Compositional Convolutional Neural Networks. IJCAI 2021: 2971-2978 - [c28]Jie Ren, Die Zhang, Yisen Wang, Lu Chen, Zhanpeng Zhou, Yiting Chen, Xu Cheng, Xin Wang, Meng Zhou, Jie Shi, Quanshi Zhang:
Towards a Unified Game-Theoretic View of Adversarial Perturbations and Robustness. NeurIPS 2021: 3797-3810 - [c27]Mingjie Li, Shaobo Wang, Quanshi Zhang:
Visualizing the Emergence of Intermediate Visual Patterns in DNNs. NeurIPS 2021: 6594-6607 - [c26]Wen Shen, Qihan Ren, Dongrui Liu, Quanshi Zhang:
Interpreting Representation Quality of DNNs for 3D Point Cloud Processing. NeurIPS 2021: 8857-8870 - [i50]Jie Ren, Die Zhang, Yisen Wang, Lu Chen, Zhanpeng Zhou, Xu Cheng, Xin Wang, Yiting Chen, Jie Shi, Quanshi Zhang:
Game-theoretic Understanding of Adversarially Learned Features. CoRR abs/2103.07364 (2021) - [i49]Jie Ren, Zhanpeng Zhou, Qirui Chen, Quanshi Zhang:
Learning Baseline Values for Shapley Values. CoRR abs/2105.10719 (2021) - [i48]Xu Cheng, Chuntung Chu, Yi Zheng, Jie Ren, Quanshi Zhang:
A Game-Theoretic Taxonomy of Visual Concepts in DNNs. CoRR abs/2106.10938 (2021) - [i47]Wen Shen, Zhihua Wei, Shikun Huang, Binbin Zhang, Jiaqi Fan, Ping Zhao, Quanshi Zhang:
Interpretable Compositional Convolutional Neural Networks. CoRR abs/2107.04474 (2021) - [i46]Quanshi Zhang, Tian Han, Lixin Fan, Zhanxing Zhu, Hang Su, Ying Nian Wu, Jie Ren, Hao Zhang:
Proceedings of ICML 2021 Workshop on Theoretic Foundation, Criticism, and Application Trend of Explainable AI. CoRR abs/2107.08821 (2021) - [i45]Xu Cheng, Xin Wang, Haotian Xue, Zhengyang Liang, Quanshi Zhang:
A Hypothesis for the Aesthetic Appreciation in Neural Networks. CoRR abs/2108.02646 (2021) - [i44]Xin Wang, Shuyun Lin, Hao Zhang, Yufei Zhu, Quanshi Zhang:
Interpreting Attributions and Interactions of Adversarial Attacks. CoRR abs/2108.06895 (2021) - [i43]Yuxiang Wu, Shang Wu, Xin Wang, Chengtian Lang, Quanshi Zhang, Quan Wen, Tianqi Xu:
Rapid detection and recognition of whole brain activity in a freely behaving Caenorhabditis elegans. CoRR abs/2109.10474 (2021) - [i42]Mingjie Li, Shaobo Wang, Quanshi Zhang:
Visualizing the Emergence of Intermediate Visual Patterns in DNNs. CoRR abs/2111.03505 (2021) - [i41]Wen Shen, Qihan Ren, Dongrui Liu, Quanshi Zhang:
Interpreting Representation Quality of DNNs for 3D Point Cloud Processing. CoRR abs/2111.03549 (2021) - [i40]Jie Ren, Mingjie Li, Qirui Chen, Huiqi Deng, Quanshi Zhang:
Towards Axiomatic, Hierarchical, and Symbolic Explanation for Deep Models. CoRR abs/2111.06206 (2021) - [i39]Huiqi Deng, Qihan Ren, Xu Chen, Hao Zhang, Jie Ren, Quanshi Zhang:
Discovering and Explaining the Representation Bottleneck of DNNs. CoRR abs/2111.06236 (2021) - [i38]Dongrui Liu, Shaobo Wang, Jie Ren, Kangrui Wang, Sheng Yin, Quanshi Zhang:
Trap of Feature Diversity in the Learning of MLPs. CoRR abs/2112.00980 (2021) - 2020
- [c25]Xu Cheng, Zhefan Rao, Yilan Chen, Quanshi Zhang:
Explaining Knowledge Distillation by Quantifying the Knowledge. CVPR 2020: 12922-12932 - [c24]Wen Shen, Binbin Zhang, Shikun Huang, Zhihua Wei, Quanshi Zhang:
3D-Rotation-Equivariant Quaternion Neural Networks. ECCV (20) 2020: 531-547 - [c23]Ruofan Liang, Tianlin Li, Longfei Li, Jing Wang, Quanshi Zhang:
Knowledge Consistency between Neural Networks and Beyond. ICLR 2020 - [c22]Liyao Xiang, Hao Zhang, Haotian Ma, Yifan Zhang, Jie Ren, Quanshi Zhang:
Interpretable Complex-Valued Neural Networks for Privacy Protection. ICLR 2020 - [i37]Xu Cheng, Zhefan Rao, Yilan Chen, Quanshi Zhang:
Explaining Knowledge Distillation by Quantifying the Knowledge. CoRR abs/2003.03622 (2020) - [i36]Hao Zhang, Yiting Chen, Liyao Xiang, Haotian Ma, Jie Shi, Quanshi Zhang:
Deep Quaternion Features for Privacy Protection. CoRR abs/2003.08365 (2020) - [i35]Hao Zhang, Yiting Chen, Haotian Ma, Xu Cheng, Qihan Ren, Liyao Xiang, Jie Shi, Quanshi Zhang:
Rotation-Equivariant Neural Networks for Privacy Protection. CoRR abs/2006.13016 (2020) - [i34]Jie Ren, Mingjie Li, Zexu Liu, Quanshi Zhang:
Interpreting and Disentangling Feature Components of Various Complexity from DNNs. CoRR abs/2006.15920 (2020) - [i33]Die Zhang, Huilin Zhou, Xiaoyi Bao, Da Huo, Ruizhao Chen, Xu Cheng, Hao Zhang, Mengyue Wu, Quanshi Zhang:
Interpreting Hierarchical Linguistic Interactions in DNNs. CoRR abs/2007.04298 (2020) - [i32]Shufan Wang, Ningyi Liao, Liyao Xiang, Nanyang Ye, Quanshi Zhang:
Achieving Adversarial Robustness via Sparsity. CoRR abs/2009.05423 (2020) - [i31]Hao Zhang, Sen Li, Yinchao Ma, Mingjie Li, Yichen Xie, Quanshi Zhang:
Interpreting and Boosting Dropout from a Game-Theoretic View. CoRR abs/2009.11729 (2020) - [i30]Xin Wang, Jie Ren, Shuyun Lin, Xiangming Zhu, Yisen Wang, Quanshi Zhang:
A Unified Approach to Interpreting and Boosting Adversarial Transferability. CoRR abs/2010.04055 (2020) - [i29]Hao Zhang, Yichen Xie, Longjie Zheng, Die Zhang, Quanshi Zhang:
Interpreting Multivariate Interactions in DNNs. CoRR abs/2010.05045 (2020) - [i28]Hao Zhang, Xu Cheng, Yiting Chen, Quanshi Zhang:
Game-Theoretic Interactions of Different Orders. CoRR abs/2010.14978 (2020)
2010 – 2019
- 2019
- [j11]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, Kokil Jaidka, Pavan Kapanipathi, Sarah Keren, Seokhwan Kim, Marc Lanctot, Danny Lange, Julian J. McAuley, David R. Martinez, Marwan Mattar, Mausam, Martin Michalowski, Reuth Mirsky, Roozbeh Mottaghi, Joseph C. Osborn, Julien Pérolat, Martin Schmid, Arash Shaban-Nejad, Onn Shehory, Biplav Srivastava, William W. Streilein, Kartik Talamadupula, Julian Togelius, Koichiro Yoshino, Quanshi Zhang, Imed Zitouni:
Reports of the Workshops Held at the 2019 AAAI Conference on Artificial Intelligence. AI Mag. 40(3): 67-78 (2019) - [j10]Quanshi Zhang, Xuan Song, Yu Yang, Haotian Ma, Ryosuke Shibasaki:
Visual graph mining for graph matching. Comput. Vis. Image Underst. 178: 16-29 (2019) - [c21]Quanshi Zhang, Yu Yang, Haotian Ma, Ying Nian Wu:
Interpreting CNNs via Decision Trees. CVPR 2019: 6261-6270 - [c20]Runjin Chen, Hao Chen, Ge Huang, Jie Ren, Quanshi Zhang:
Explaining Neural Networks Semantically and Quantitatively. ICCV 2019: 9186-9195 - [c19]Chaoyu Guan, Xiting Wang, Quanshi Zhang, Runjin Chen, Di He, Xing Xie:
Towards a Deep and Unified Understanding of Deep Neural Models in NLP. ICML 2019: 2454-2463 - [i27]Zenan Ling, Haotian Ma, Yu Yang, Robert C. Qiu, Song-Chun Zhu, Quanshi Zhang:
Explaining AlphaGo: Interpreting Contextual Effects in Neural Networks. CoRR abs/1901.02184 (2019) - [i26]Quanshi Zhang, Ying Nian Wu, Song-Chun Zhu:
Interpretable CNNs. CoRR abs/1901.02413 (2019) - [i25]Quanshi Zhang, Yu Yang, Qian Yu, Ying Nian Wu:
Network Transplanting (extended abstract). CoRR abs/1901.06978 (2019) - [i24]Quanshi Zhang, Yu Yang, Ying Nian Wu:
Unsupervised Learning of Neural Networks to Explain Neural Networks (extended abstract). CoRR abs/1901.07538 (2019) - [i23]Quanshi Zhang, Lixin Fan, Bolei Zhou:
Proceedings of AAAI 2019 Workshop on Network Interpretability for Deep Learning. CoRR abs/1901.08813 (2019) - [i22]Liyao Xiang, Haotian Ma, Hao Zhang, Yifan Zhang, Quanshi Zhang:
Complex-Valued Neural Networks for Privacy Protection. CoRR abs/1901.09546 (2019) - [i21]Haotian Ma, Yinqing Zhang, Fan Zhou, Quanshi Zhang:
Quantifying Layerwise Information Discarding of Neural Networks. CoRR abs/1906.04109 (2019) - [i20]Ruofan Liang, Tianlin Li, Longfei Li, Quanshi Zhang:
Knowledge Isomorphism between Neural Networks. CoRR abs/1908.01581 (2019) - [i19]Hao Zhang, Jiayi Chen, Haotian Xue, Quanshi Zhang:
Towards a Unified Evaluation of Explanation Methods without Ground Truth. CoRR abs/1911.09017 (2019) - [i18]Binbin Zhang, Wen Shen, Shikun Huang, Zhihua Wei, Quanshi Zhang:
3D-Rotation-Equivariant Quaternion Neural Networks. CoRR abs/1911.09040 (2019) - [i17]Shikun Huang, Binbin Zhang, Wen Shen, Zhihua Wei, Quanshi Zhang:
Utility Analysis of Network Architectures for 3D Point Cloud Processing. CoRR abs/1911.09053 (2019) - [i16]Shuang Zhang, Liyao Xiang, Congcong Li, Yixuan Wang, Zeyu Liu, Quanshi Zhang, Bo Li:
Preventing Information Leakage with Neural Architecture Search. CoRR abs/1912.08421 (2019) - 2018
- [j9]Quanshi Zhang, Ying Nian Wu, Hao Zhang, Song-Chun Zhu:
Mining deep And-Or object structures via cost-sensitive question-answer-based active annotations. Comput. Vis. Image Underst. 176-177: 33-44 (2018) - [j8]Quanshi Zhang, Song-Chun Zhu:
Visual interpretability for deep learning: a survey. Frontiers Inf. Technol. Electron. Eng. 19(1): 27-39 (2018) - [c18]Quanshi Zhang, Ruiming Cao, Feng Shi, Ying Nian Wu, Song-Chun Zhu:
Interpreting CNN Knowledge via an Explanatory Graph. AAAI 2018: 4454-4463 - [c17]Quanshi Zhang, Wenguan Wang, Song-Chun Zhu:
Examining CNN Representations With Respect to Dataset Bias. AAAI 2018: 4464-4473 - [c16]Quanshi Zhang, Ying Nian Wu, Song-Chun Zhu:
Interpretable Convolutional Neural Networks. CVPR 2018: 8827-8836 - [i15]Quanshi Zhang, Yu Yang, Ying Nian Wu, Song-Chun Zhu:
Interpreting CNNs via Decision Trees. CoRR abs/1802.00121 (2018) - [i14]Quanshi Zhang, Song-Chun Zhu:
Visual Interpretability for Deep Learning: a Survey. CoRR abs/1802.00614 (2018) - [i13]Quanshi Zhang, Yu Yang, Ying Nian Wu, Song-Chun Zhu:
Network Transplanting. CoRR abs/1804.10272 (2018) - [i12]Quanshi Zhang, Yu Yang, Yuchen Liu, Ying Nian Wu, Song-Chun Zhu:
Unsupervised Learning of Neural Networks to Explain Neural Networks. CoRR abs/1805.07468 (2018) - [i11]Runjin Chen, Hao Chen, Ge Huang, Jie Ren, Quanshi Zhang:
Explaining Neural Networks Semantically and Quantitatively. CoRR abs/1812.07169 (2018) - [i10]Quanshi Zhang, Ruiming Cao, Ying Nian Wu, Song-Chun Zhu:
Mining Interpretable AOG Representations from Convolutional Networks via Active Question Answering. CoRR abs/1812.07996 (2018) - [i9]Quanshi Zhang, Xin Wang, Ruiming Cao, Ying Nian Wu, Feng Shi, Song-Chun Zhu:
Explanatory Graphs for CNNs. CoRR abs/1812.07997 (2018) - 2017
- [j7]Xuan Song, Quanshi Zhang, Yoshihide Sekimoto, Ryosuke Shibasaki, Nicholas Jing Yuan, Xing Xie:
Prediction and Simulation of Human Mobility Following Natural Disasters. ACM Trans. Intell. Syst. Technol. 8(2): 29:1-29:23 (2017) - [c15]Quanshi Zhang, Ruiming Cao, Ying Nian Wu, Song-Chun Zhu:
Growing Interpretable Part Graphs on ConvNets via Multi-Shot Learning. AAAI 2017: 2898-2906 - [c14]Quanshi Zhang, Ruiming Cao, Ying Nian Wu, Song-Chun Zhu:
Mining Object Parts from CNNs via Active Question-Answering. CVPR 2017: 3890-3899 - [i8]Quanshi Zhang, Ruiming Cao, Ying Nian Wu, Song-Chun Zhu:
Mining Object Parts from CNNs via Active Question-Answering. CoRR abs/1704.03173 (2017) - [i7]Quanshi Zhang, Ruiming Cao, Shengming Zhang, Mark Edmonds, Ying Nian Wu, Song-Chun Zhu:
Interactively Transferring CNN Patterns for Part Localization. CoRR abs/1708.01783 (2017) - [i6]Quanshi Zhang, Ruiming Cao, Feng Shi, Ying Nian Wu, Song-Chun Zhu:
Interpreting CNN knowledge via an Explanatory Graph. CoRR abs/1708.01785 (2017) - [i5]Quanshi Zhang, Ying Nian Wu, Song-Chun Zhu:
A Cost-Sensitive Visual Question-Answer Framework for Mining a Deep And-OR Object Semantics from Web Images. CoRR abs/1708.03911 (2017) - [i4]Quanshi Zhang, Xuan Song, Ryosuke Shibasaki:
Visual Graph Mining. CoRR abs/1708.03921 (2017) - [i3]Quanshi Zhang, Ying Nian Wu, Song-Chun Zhu:
Interpretable Convolutional Neural Networks. CoRR abs/1710.00935 (2017) - [i2]Quanshi Zhang, Wenguan Wang, Song-Chun Zhu:
Examining CNN representations with respect to Dataset Bias. CoRR abs/1710.10577 (2017) - 2016
- [j6]Quanshi Zhang, Xuan Song, Xiaowei Shao, Huijing Zhao, Ryosuke Shibasaki:
Object Discovery: Soft Attributed Graph Mining. IEEE Trans. Pattern Anal. Mach. Intell. 38(3): 532-545 (2016) - [i1]Quanshi Zhang, Ruiming Cao, Ying Nian Wu, Song-Chun Zhu:
Multi-Shot Mining Semantic Part Concepts in CNNs. CoRR abs/1611.04246 (2016) - 2015
- [j5]Quanshi Zhang, Xuan Song, Xiaowei Shao, Huijing Zhao, Ryosuke Shibasaki:
From RGB-D Images to RGB Images: Single Labeling for Mining Visual Models. ACM Trans. Intell. Syst. Technol. 6(2): 16:1-16:29 (2015) - [c13]Xuan Song, Quanshi Zhang, Yoshihide Sekimoto, Ryosuke Shibasaki, Nicholas Jing Yuan, Xing Xie:
A Simulator of Human Emergency Mobility Following Disasters: Knowledge Transfer from Big Disaster Data. AAAI 2015: 730-736 - [c12]Quanshi Zhang, Ying Nian Wu, Song-Chun Zhu:
Mining And-Or Graphs for Graph Matching and Object Discovery. ICCV 2015: 55-63 - 2014
- [c11]Xuan Song, Quanshi Zhang, Yoshihide Sekimoto, Ryosuke Shibasaki:
Intelligent System for Urban Emergency Management during Large-Scale Disaster. AAAI 2014: 458-464 - [c10]Quanshi Zhang, Xuan Song, Xiaowei Shao, Huijing Zhao, Ryosuke Shibasaki:
When 3D Reconstruction Meets Ubiquitous RGB-D Images. CVPR 2014: 700-707 - [c9]Quanshi Zhang, Xuan Song, Xiaowei Shao, Huijing Zhao, Ryosuke Shibasaki:
Attributed Graph Mining and Matching: An Attempt to Define and Extract Soft Attributed Patterns. CVPR 2014: 1394-1401 - [c8]Quanshi Zhang, Xuan Song, Xiaowei Shao, Huijing Zhao, Ryosuke Shibasaki:
Start from minimum labeling: Learning of 3D object models and point labeling from a large and complex environment. ICRA 2014: 3082-3089 - [c7]Xuan Song, Quanshi Zhang, Yoshihide Sekimoto, Ryosuke Shibasaki:
Prediction of human emergency behavior and their mobility following large-scale disaster. KDD 2014: 5-14 - 2013
- [j4]Xuan Song, Quanshi Zhang, Yoshihide Sekimoto, Teerayut Horanont, Satoshi Ueyama, Ryosuke Shibasaki:
Intelligent System for Human Behavior Analysis and Reasoning Following Large-Scale Disasters. IEEE Intell. Syst. 28(4): 35-42 (2013) - [j3]Quanshi Zhang, Xuan Song, Xiaowei Shao, Ryosuke Shibasaki, Huijing Zhao:
Unsupervised skeleton extraction and motion capture from 3D deformable matching. Neurocomputing 100: 170-182 (2013) - [j2]Xuan Song, Xiaowei Shao, Quanshi Zhang, Ryosuke Shibasaki, Huijing Zhao, Hongbin Zha:
A novel dynamic model for multiple pedestrians tracking in extremely crowded scenarios. Inf. Fusion 14(3): 301-310 (2013) - [j1]Xuan Song, Xiaowei Shao, Quanshi Zhang, Ryosuke Shibasaki, Huijing Zhao, Jinshi Cui, Hongbin Zha:
A fully online and unsupervised system for large and high-density area surveillance: Tracking, semantic scene learning and abnormality detection. ACM Trans. Intell. Syst. Technol. 4(2): 35:1-35:21 (2013) - [c6]Quanshi Zhang, Xuan Song, Xiaowei Shao, Ryosuke Shibasaki, Huijing Zhao:
Category Modeling from Just a Single Labeling: Use Depth Information to Guide the Learning of 2D Models. CVPR 2013: 193-200 - [c5]Quanshi Zhang, Xuan Song, Xiaowei Shao, Huijing Zhao, Ryosuke Shibasaki:
Learning Graph Matching: Oriented to Category Modeling from Cluttered Scenes. ICCV 2013: 1329-1336 - [c4]Quanshi Zhang, Xuan Song, Xiaowei Shao, Huijing Zhao, Ryosuke Shibasaki:
Unsupervised 3D category discovery and point labeling from a large urban environment. ICRA 2013: 2685-2692 - [c3]Xuan Song, Quanshi Zhang, Yoshihide Sekimoto, Teerayut Horanont, Satoshi Ueyama, Ryosuke Shibasaki:
Modeling and probabilistic reasoning of population evacuation during large-scale disaster. KDD 2013: 1231-1239 - 2012
- [c2]Xuan Song, Xiaowei Shao, Quanshi Zhang, Ryosuke Shibasaki, Huijing Zhao, Hongbin Zha:
Laser-based intelligent surveillance and abnormality detection in extremely crowded scenarios. ICRA 2012: 2170-2176
2000 – 2009
- 2009
- [c1]Huijing Zhao, Quanshi Zhang, Masaki Chiba, Ryosuke Shibasaki, Jinshi Cui, Hongbin Zha:
Moving object classification using horizontal laser scan data. ICRA 2009: 2424-2430
Coauthor Index
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