Xu Cheng

Orcid: 0000-0002-3317-1020

Affiliations:
  • Shanghai Jiao Tong University, Shanghai, China


According to our database1, Xu Cheng authored at least 15 papers between 2020 and 2024.

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

Timeline

Legend:

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PhD thesis 
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Bibliography

2024
Clarifying the Behavior and the Difficulty of Adversarial Training.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
Quantifying the Knowledge in a DNN to Explain Knowledge Distillation for Classification.
IEEE Trans. Pattern Anal. Mach. Intell., April, 2023

Network Transplanting for the Functionally Modular Architecture.
Proceedings of the Pattern Recognition and Computer Vision - 6th Chinese Conference, 2023

Towards the Difficulty for a Deep Neural Network to Learn Concepts of Different Complexities.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 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

2021
A Hypothesis for the Aesthetic Appreciation in Neural Networks.
CoRR, 2021

A Game-Theoretic Taxonomy of Visual Concepts in DNNs.
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

Building Interpretable Interaction Trees for Deep NLP Models.
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020
Game-Theoretic Interactions of Different Orders.
CoRR, 2020

Interpreting Hierarchical Linguistic Interactions in DNNs.
CoRR, 2020

Rotation-Equivariant Neural Networks for Privacy Protection.
CoRR, 2020

Explaining Knowledge Distillation by Quantifying the Knowledge.
Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020


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