1.9k citations · 2k across the 8 of their papers we have counts for
8 papers
Xe-Forge: Multi-Stage LLM-Powered Kernel Optimization for Intel GPU
Marcin Spoczynski, Daniel Fleischer, Moshe Berchansky +5
Porting deep learning algorithms to new hardware accelerators requires developers to repeatedly apply the same low-level optimizations -- quantization, memory access coalescing, ti…
Robust Principles: Architectural Design Principles for Adversarially Robust CNNs
ShengYun Peng, Weilin Xu, Cory Cornelius +6
Our research aims to unify existing works' diverging opinions on how architectural components affect the adversarial robustness of CNNs. To accomplish our goal, we synthesize a sui…
RobArch: Designing Robust Architectures against Adversarial Attacks
ShengYun Peng, Weilin Xu, Cory Cornelius +4
Adversarial Training is the most effective approach for improving the robustness of Deep Neural Networks (DNNs). However, compared to the large body of research in optimizing the a…
Membership-Doctor: Comprehensive Assessment of Membership Inference Against Machine Learning Models
Xinlei He, Zheng Li, Weilin Xu +2
Machine learning models are prone to memorizing sensitive data, making them vulnerable to membership inference attacks in which an adversary aims to infer whether an input sample w…
Security Risks in Deep Learning Implementations
Qixue Xiao, Kang Li, Deyue Zhang +1
Advance in deep learning algorithms overshadows their security risk in software implementations. This paper discloses a set of vulnerabilities in popular deep learning frameworks i…
Feature Squeezing Mitigates and Detects Carlini/Wagner Adversarial Examples
Weilin Xu, David Evans, Yanjun Qi
Feature squeezing is a recently-introduced framework for mitigating and detecting adversarial examples. In previous work, we showed that it is effective against several earlier met…
Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks
Weilin Xu, David Evans, Yanjun Qi
Although deep neural networks (DNNs) have achieved great success in many tasks, they can often be fooled by \emph{adversarial examples} that are generated by adding small but purpo…
DeepCloak: Masking Deep Neural Network Models for Robustness Against Adversarial Samples
Ji Gao, Beilun Wang, Zeming Lin +2
Recent studies have shown that deep neural networks (DNN) are vulnerable to adversarial samples: maliciously-perturbed samples crafted to yield incorrect model outputs. Such attack…