With the widespread deployment of deep neural networks (DNNs), ensuring ...
Federated learning provides an effective paradigm to jointly optimize a ...
Recent studies show that training deep neural networks (DNNs) with Lipsc...
A vast literature shows that the learning-based visual perception model ...
Despite great recent advances achieved by deep neural networks (DNNs), t...
Bound propagation methods, when combined with branch and bound, are amon...
Federated learning (FL) provides an effective paradigm to train machine
...
Neural networks (NNs) are known to be vulnerable against adversarial
per...
With the rapid development of deep learning, the sizes of neural network...
Automatic machine learning, or AutoML, holds the promise of truly
democr...
Certifying the robustness of model performance under bounded data
distri...
Point cloud models with neural network architectures have achieved great...
We present the first framework of Certifying Robust Policies for
reinfor...
Boundary based blackbox attack has been recognized as practical and
effe...
Adversarial Transferability is an intriguing property of adversarial exa...
Gradient estimation and vector space projection have been studied as two...
As adversarial attacks against machine learning models have raised incre...
Great advancement in deep neural networks (DNNs) has led to state-of-the...
As machine learning systems become pervasive, safeguarding their securit...
We study the problem of explaining a rich class of behavioral properties...
In this report, we applied integrated gradients to explaining a neural
n...