1.8k citations · 2.4k across the 78 of their papers we have counts for
6 papers · 2 filters
CNN-Cert: An Efficient Framework for Certifying Robustness of Convolutional Neural Networks
Akhilan Boopathy, Tsui-Wei Weng, Pin-Yu Chen +2
Verifying robustness of neural network classifiers has attracted great interests and attention due to the success of deep neural networks and their unexpected vulnerability to adve…
Is Ordered Weighted Regularized Regression Robust to Adversarial Perturbation? A Case Study on OSCAR
Pin-Yu Chen, Bhanukiran Vinzamuri, Sijia Liu
Many state-of-the-art machine learning models such as deep neural networks have recently shown to be vulnerable to adversarial perturbations, especially in classification tasks. Mo…
Fast Incremental von Neumann Graph Entropy Computation: Theory, Algorithm, and Applications
Pin-Yu Chen, Lingfei Wu, Sijia Liu +1
The von Neumann graph entropy (VNGE) facilitates measurement of information divergence and distance between graphs in a graph sequence. It has been successfully applied to various…
On the Supermodularity of Active Graph-based Semi-supervised Learning with Stieltjes Matrix Regularization
Pin-Yu Chen, Dennis Wei
Active graph-based semi-supervised learning (AG-SSL) aims to select a small set of labeled examples and utilize their graph-based relation to other unlabeled examples to aid in mac…
Bypassing Feature Squeezing by Increasing Adversary Strength
Yash Sharma, Pin-Yu Chen
Feature Squeezing is a recently proposed defense method which reduces the search space available to an adversary by coalescing samples that correspond to many different feature vec…
Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach
Tsui-Wei Weng, Huan Zhang, Pin-Yu Chen +5
The robustness of neural networks to adversarial examples has received great attention due to security implications. Despite various attack approaches to crafting visually impercep…