4 citations · 7 across the 6 of their papers we have counts for
6 papers
Understanding Certified Training with Interval Bound Propagation
Yuhao Mao, Mark Niklas Müller, Marc Fischer +1
As robustness verification methods are becoming more precise, training certifiably robust neural networks is becoming ever more relevant. To this end, certified training methods co…
SMAP: A Novel Heterogeneous Information Framework for Scenario-based Optimal Model Assignment
Zekun Qiu, Zhipu Xie, Zehua Ji +2
The increasing maturity of big data applications has led to a proliferation of models targeting the same objectives within the same scenarios and datasets. However, selecting the m…
TAPS: Connecting Certified and Adversarial Training
Yuhao Mao, Mark Niklas Müller, Marc Fischer +1
Training certifiably robust neural networks remains a notoriously hard problem. On one side, adversarial training optimizes under-approximations of the worst-case loss, which leads…
"Is your explanation stable?": A Robustness Evaluation Framework for Feature Attribution
Yuyou Gan, Yuhao Mao, Xuhong Zhang +5
Understanding the decision process of neural networks is hard. One vital method for explanation is to attribute its decision to pivotal features. Although many algorithms are propo…
Transfer Attacks Revisited: A Large-Scale Empirical Study in Real Computer Vision Settings
Yuhao Mao, Chong Fu, Saizhuo Wang +7
One intriguing property of adversarial attacks is their "transferability" -- an adversarial example crafted with respect to one deep neural network (DNN) model is often found effec…
Application of Knowledge Graphs to Provide Side Information for Improved Recommendation Accuracy
Yuhao Mao, Serguei A. Mokhov, Sudhir P. Mudur
Personalized recommendations are popular in these days of Internet driven activities, specifically shopping. Recommendation methods can be grouped into three major categories, cont…