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ORCIDJie Ren 0018
Other persons with the same name
- Jie Ren — disambiguation page
- Jie Ren 0001 — Google Inc., Santa Monica, CA, USA (and 1 more)
- Jie Ren 0002
![[0000-0001-5931-5881]](https://dblp.dagstuhl.de/img/orcid-mark.12x12.png "0000-0001-5931-5881")
— Nantong University, School of Information Science and Technology, Nantong, China - Jie Ren 0003 — University of Electronic Science and Technology of China, Chengdu, China
- Jie Ren 0004 — Beihang University, School of Electronic and Information Engineering, Beijing, China
- Jie Ren 0005 — National University of Singapore, Department of Physics, Singapore
- Jie Ren 0006 — Google Brain, CA, USA (and 2 more)
- Jie Ren 0007 — Shaanxi Normal University, School of Computer Science, Xi'an, China (and 1 more)
- Jie Ren 0008 — University of Toronto, ON, Canada (and 1 more)
- Jie Ren 0009 — Fordham University, Gabelli School of Business, New York, NY, USA (and 1 more)
- Jie Ren 0010 — Beijing Jiaotong University, School of Electronics and Information Engineering, Beijing, China
- Jie Ren 0011 — China Yangtze Power Co., Yichang, China
- Jie Ren 0012 — Peking University, Institute of Computer Science and Technology, Beijing, China
- Jie Ren 0013 — Huawei Technologies, Shanghai, China (and 2 more)
- Jie Ren 0014 — Xi'an Polytechnic University, College of Electronics and Information, Xi'an, China (and 1 more)
- Jie Ren 0015 — College of William & Mary, Williamsburg, VA, USA (and 1 more)
- Jie Ren 0016 — Harbin Institute of Technology, School of Computer Science and Technology, Harbin, China
- Jie Ren 0017 — Tsinghua University, Beijing, China
- Jie Ren 0019 — Michigan State University, Department of Computer Science and Engineering, East Lansing, MI, USA
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2020 – today
- 2025
[c10]Jie Ren, Xinhao Zheng, Jiyu Liu, Andrew Lizarraga, Ying Nian Wu, Liang Lin, Quanshi Zhang:
Monitoring Primitive Interactions During the Training of DNNs. AAAI 2025: 20183-20191- 2024
- [j2]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) - [c9]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 - [i14]Jie Ren, Qipeng Guo, Hang Yan, Dongrui Liu, Xipeng Qiu, Dahua Lin:
Identifying Semantic Induction Heads to Understand In-Context Learning. CoRR abs/2402.13055 (2024) - 2023
- [c8]Jie Ren, Mingjie Li, Qirui Chen, Huiqi Deng, Quanshi Zhang:
Defining and Quantifying the Emergence of Sparse Concepts in DNNs. CVPR 2023: 20280-20289 - [c7]Jie Ren, Zhanpeng Zhou, Qirui Chen, Quanshi Zhang:
Can We Faithfully Represent Absence States to Compute Shapley Values on a DNN? ICLR 2023 - 2022
- [c6]Jie Ren, Mingjie Li, Meng Zhou, Shih-Han Chan, Quanshi Zhang:
Towards Theoretical Analysis of Transformation Complexity of ReLU DNNs. ICML 2022: 18537-18558 - [i13]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) - [i12]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) - [i11]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) - 2021
- [j1]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) - [c5]Xin Wang, Jie Ren, Shuyun Lin, Xiangming Zhu, Yisen Wang, Quanshi Zhang:
A Unified Approach to Interpreting and Boosting Adversarial Transferability. ICLR 2021 - [c4]Jie Ren, Mingjie Li, Zexu Liu, Quanshi Zhang:
Interpreting and Disentangling Feature Components of Various Complexity from DNNs. ICML 2021: 8971-8981 - [c3]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 - [i10]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) - [i9]Jie Ren, Zhanpeng Zhou, Qirui Chen, Quanshi Zhang:
Learning Baseline Values for Shapley Values. CoRR abs/2105.10719 (2021) - [i8]Xu Cheng, Chuntung Chu, Yi Zheng, Jie Ren, Quanshi Zhang:
A Game-Theoretic Taxonomy of Visual Concepts in DNNs. CoRR abs/2106.10938 (2021) - [i7]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) - [i6]Jie Ren, Mingjie Li, Qirui Chen, Huiqi Deng, Quanshi Zhang:
Towards Axiomatic, Hierarchical, and Symbolic Explanation for Deep Models. CoRR abs/2111.06206 (2021) - [i5]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) - [i4]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
- [c2]Liyao Xiang, Hao Zhang, Haotian Ma, Yifan Zhang, Jie Ren, Quanshi Zhang:
Interpretable Complex-Valued Neural Networks for Privacy Protection. ICLR 2020 - [i3]Jie Ren, Mingjie Li, Zexu Liu, Quanshi Zhang:
Interpreting and Disentangling Feature Components of Various Complexity from DNNs. CoRR abs/2006.15920 (2020) - [i2]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) - no results
2010 – 2019
- 2019
- [c1]Runjin Chen, Hao Chen, Ge Huang, Jie Ren, Quanshi Zhang:
Explaining Neural Networks Semantically and Quantitatively. ICCV 2019: 9186-9195 - 2018
- [i1]Runjin Chen, Hao Chen, Ge Huang, Jie Ren, Quanshi Zhang:
Explaining Neural Networks Semantically and Quantitatively. CoRR abs/1812.07169 (2018) - no results
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