Jie Ren

Orcid: 0000-0001-9918-3000

Affiliations:

Shanghai Jiao Tong University, China

According to our database¹, Jie Ren authored at least 22 papers between 2019 and 2025.

Collaborative distances:

Dijkstra number² of five.
Erdős number³ of four.

Timeline

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Bibliography

2025

Monitoring Primitive Interactions During the Training of DNNs.

[BibT_eX]

[DOI]

Proceedings of the AAAI-25, Sponsored by the Association for the Advancement of Artificial Intelligence, February 25, 2025

2024

Interpretability of Neural Networks Based on Game-theoretic Interactions.

[BibT_eX]

[DOI]

Mach. Intell. Res., August, 2024

Identifying Semantic Induction Heads to Understand In-Context Learning.

[BibT_eX]

[DOI]

CoRR, 2024

Identifying Semantic Induction Heads to Understand In-Context Learning.

[BibT_eX]

[DOI]

Proceedings of the Findings of the Association for Computational Linguistics, 2024

2023

Can We Faithfully Represent Absence States to Compute Shapley Values on a DNN?

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Defining and Quantifying the Emergence of Sparse Concepts in DNNs.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022

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

[BibT_eX]

[DOI]

CoRR, 2022

Why Adversarial Training of ReLU Networks Is Difficult?

[BibT_eX]

[DOI]

CoRR, 2022

Towards Theoretical Analysis of Transformation Complexity of ReLU DNNs.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2022

2021

Mining Interpretable AOG Representations From Convolutional Networks via Active Question Answering.

[BibT_eX]

[DOI]

IEEE Trans. Pattern Anal. Mach. Intell., 2021

Trap of Feature Diversity in the Learning of MLPs.

[BibT_eX]

[DOI]

CoRR, 2021

Discovering and Explaining the Representation Bottleneck of DNNs.

[BibT_eX]

[DOI]

CoRR, 2021

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

[BibT_eX]

[DOI]

CoRR, 2021

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

[BibT_eX]

[DOI]

CoRR, 2021

A Game-Theoretic Taxonomy of Visual Concepts in DNNs.

[BibT_eX]

[DOI]

CoRR, 2021

Learning Baseline Values for Shapley Values.

[BibT_eX]

[DOI]

CoRR, 2021

Game-theoretic Understanding of Adversarially Learned Features.

[BibT_eX]

[DOI]

CoRR, 2021

Towards a Unified Game-Theoretic View of Adversarial Perturbations and Robustness.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Proceedings of the 38th International Conference on Machine Learning, 2021

A Unified Approach to Interpreting and Boosting Adversarial Transferability.

[BibT_eX]

[DOI]

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Interpretable Complex-Valued Neural Networks for Privacy Protection.

[BibT_eX]

[DOI]

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Explaining Neural Networks Semantically and Quantitatively.

[BibT_eX]

[DOI]

Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019

Jie Ren

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