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Using AI to develop enhanced cybersecurity measures

Using AI to develop enhanced cybersecurity measures
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Los Alamos National Laboratory , Research In Cyber Systems At Los Alamos , Microsoft Windows , Maksim Eren , Advanced Research , Cyber Systems , Semi Supervised Classification , Malware Families Under Extreme Class Imbalance , Hierarchical Non Negative Matrix Factorization , Automatic Model Selection ,

GitHub - continuousml/Awesome-Out-Of-Distribution-Detection: A professionally curated list of papers, tutorials, books, videos, articles and open-source libraries etc for Out-of-distribution detection, robustness, and generalization

A professionally curated list of papers, tutorials, books, videos, articles and open-source libraries etc for Out-of-distribution detection, robustness, and generalization - GitHub - continuousml/Awesome-Out-Of-Distribution-Detection: A professionally curated list of papers, tutorials, books, videos, articles and open-source libraries etc for Out-of-distribution detection, robustness, and generalization ....

Sri Lanka , Dustin Tran , Thomasg Dietterich , Alexander Meinke , Yibo Zhou , Jie Ren , Vaishnavh Nagarajan , Balaji Lakshminarayanan , Neeraj Varshney , Sharon Yixuan Li , Jasper Snoek , Examples In Neural Networks , Presence Of Limited , Mitigating Neural Network Overconfidence , Confidence For Detection In Neural Networks , Energy Regularization Loss For Detection , Reliability Of Image Detection In Neural Networks , Detection In Deep Neural Networks , Detection Using Union , Networks Can Improve Robustness , Hopfield Energy , A Unified Survey On , Detection For Graph Neural Networks , Energy Based Model , Neural Networks , Why Relu Networks Yield High ,

"MutexMatch: Semi-Supervised Learning With Mutex-Based Consistency Regu" by Yue Duan, Zhen Zhao et al.

The core issue in semi-supervised learning (SSL) lies in how to effectively leverage unlabeled data, whereas most existing methods tend to put a great emphasis on the utilization of high-confidence samples yet seldom fully explore the usage of low-confidence samples. In this article, we aim to utilize low-confidence samples in a novel way with our proposed mutex-based consistency regularization, namely MutexMatch. Specifically, the high-confidence samples are required to exactly predict “what it is” by the conventional true-positive classifier (TPC), while low-confidence samples are employed to achieve a simpler goal to predict with ease “what it is not” by the true-negative classifier (TNC). In this sense, we not only mitigate the pseudo-labeling errors but also make full use of the low-confidence unlabeled data by the consistency of dissimilarity degree. MutexMatch achieves superior performance on multiple benchmark datasets, i.e., Canadian Institute for Advanced Research (CI ....

Canadian Institute For Advanced Research , Canadian Institute , Advanced Research , Data Models , Utex Based Consistency Regularization , Predictive Models , Semi Supervised Classification , Semisupervised Learning , Task Analysis ,