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20202024
most citedProbabilistic Verification of Neural Networks Against Group Fairness

3 citations · 7 across the 9 of their papers we have counts for

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5 papers · 1 filter

cs.LG2024

Patch Synthesis for Property Repair of Deep Neural Networks

Zhiming Chi, Jianan Ma, Pengfei Yang +4

Deep neural networks (DNNs) are prone to various dependability issues, such as adversarial attacks, which hinder their adoption in safety-critical domains. Recently, NN repair tech…

cs.LG2024

DeepCDCL: An CDCL-based Neural Network Verification Framework

Zongxin Liu, Pengfei Yang, Lijun Zhang +1

Neural networks in safety-critical applications face increasing safety and security concerns due to their susceptibility to little disturbance. In this paper, we propose DeepCDCL,…

cs.LG2021★ 3 cited

Probabilistic Verification of Neural Networks Against Group Fairness

Bing Sun, Jun Sun, Ting Dai +1

Fairness is crucial for neural networks which are used in applications with important societal implication. Recently, there have been multiple attempts on improving fairness of neu…

cs.LG2021★ 3 cited

Ensemble Defense with Data Diversity: Weak Correlation Implies Strong Robustness

Renjue Li, Hanwei Zhang, Pengfei Yang +4

In this paper, we propose a framework of filter-based ensemble of deep neuralnetworks (DNNs) to defend against adversarial attacks. The framework builds an ensemble of sub-models -…

cs.LG2021

Towards Practical Robustness Analysis for DNNs based on PAC-Model Learning

Renjue Li, Pengfei Yang, Cheng-Chao Huang +3

To analyse local robustness properties of deep neural networks (DNNs), we present a practical framework from a model learning perspective. Based on black-box model learning with sc…