Jie Ren

Orcid: 0000-0001-9918-3000

Affiliations:
  • Shanghai Jiao Tong University, China


According to our database1, Jie Ren authored at least 17 papers between 2019 and 2023.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

Online presence:

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Bibliography

2023
Can We Faithfully Represent Absence States to Compute Shapley Values on a DNN?
Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022
Proving Common Mechanisms Shared by Twelve Methods of Boosting Adversarial Transferability.
CoRR, 2022

Why Adversarial Training of ReLU Networks Is Difficult?
CoRR, 2022

Towards Theoretical Analysis of Transformation Complexity of ReLU DNNs.
Proceedings of the International Conference on Machine Learning, 2022

2021
Mining Interpretable AOG Representations From Convolutional Networks via Active Question Answering.
IEEE Trans. Pattern Anal. Mach. Intell., 2021

Trap of Feature Diversity in the Learning of MLPs.
CoRR, 2021

Discovering and Explaining the Representation Bottleneck of DNNs.
CoRR, 2021

Towards Axiomatic, Hierarchical, and Symbolic Explanation for Deep Models.
CoRR, 2021

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

A Game-Theoretic Taxonomy of Visual Concepts in DNNs.
CoRR, 2021

Learning Baseline Values for Shapley Values.
CoRR, 2021

Game-theoretic Understanding of Adversarially Learned Features.
CoRR, 2021

Towards a Unified Game-Theoretic View of Adversarial Perturbations and Robustness.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Interpreting and Disentangling Feature Components of Various Complexity from DNNs.
Proceedings of the 38th International Conference on Machine Learning, 2021

A Unified Approach to Interpreting and Boosting Adversarial Transferability.
Proceedings of the 9th International Conference on Learning Representations, 2021

2020
Interpretable Complex-Valued Neural Networks for Privacy Protection.
Proceedings of the 8th International Conference on Learning Representations, 2020

2019
Explaining Neural Networks Semantically and Quantitatively.
Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019


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