ScholarIQanswers from OpenAlex
Adversarial Robustness in Machine Learning
TopicLeading institutions, researchers & key papers
Adversarial Robustness in Machine Learning is a topic indexed in ScholarIQ from OpenAlex.
What is known about Adversarial Robustness in Machine Learning?
ScholarIQrecord summary
This cluster of papers focuses on the robustness of deep learning models against adversarial attacks, exploring topics such as adversarial examples, security, uncertainty estimation, defenses, and verification. It delves into the challenges and potential solutions for ensuring the resilience of neural networks in the face of malicious inputs.
How many works does Adversarial Robustness in Machine Learning have?
ScholarIQindexed works
Adversarial Robustness in Machine Learning has 81 works in the ScholarIQ index. The count is the OpenAlex total, not the number of papers listed on this page.
What is the OpenAlex record for Adversarial Robustness in Machine Learning?
ScholarIQopenalex
The OpenAlex for Adversarial Robustness in Machine Learning is on the source record.
What are the most-cited papers on Adversarial Robustness in Machine Learning?
ScholarIQmost cited works
Understanding adversarial attacks on deep learning based medical image analysis systems
Xingjun Ma, Yuhao Niu, Lin Gu, Yisen Wang, Yitian Zhao, James Bailey, Feng Lu
Reflection Backdoor: A Natural Backdoor Attack on Deep Neural Networks
Yunfei Liu, Xingjun Ma, James Bailey, Feng Lu
Born Again Neural Networks
Tommaso Furlanello, Zachary C. Lipton, Michael Tschannen, Laurent Itti, Anima Anandkumar
Born Again Neural Networks
Tommaso Furlanello, Zachary C. Lipton, Michael Tschannen, Laurent Itti, Anima Anandkumar
Adversarial Attacks in Modulation Recognition With Convolutional Neural Networks
Yun Lin, Haojun Zhao, Xuefei Ma, Ya Tu, Meiyu Wang
Where is Adversarial Robustness in Machine Learning research published, and who funds it?
ScholarIQvenues & funding sources
TOP JOURNALS
TOP FUNDERS
National Science Foundation—
NIH—
Wellcome Trust—
European Research Council—
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How much of the research on Adversarial Robustness in Machine Learning is open access?
ScholarIQopen access share
40%OPEN ACCESS