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Found 5 papers, 4 papers with code
UN-AVOIDS: Unsupervised and Nonparametric Approach for Visualizing Outliers and Invariant Detection Scoring
Among them is designing a visualization aided anomaly detection (VAAD), a type of software that aids analysts by providing UN-AVOIDS' detection algorithm (running in a back engine), NCDF visualization space (rendered to plots), along with other conventional methods of visualization in the original feature space, all of which are linked in one interactive environment.
Classifier Calibration: with application to threat scores in cybersecurity
A calibrator is a function that maps the arbitrary classifier score, of a testing observation, onto $[0, 1]$ to provide an estimate for the posterior probability of belonging to one of the two classes.
Machine Learning in Precision Medicine to Preserve Privacy via Encryption
Precision medicine is an emerging approach for disease treatment and prevention that delivers personalized care to individual patients by considering their genetic makeups, medical histories, environments, and lifestyles.
Interpreting Machine Learning Malware Detectors Which Leverage N-gram Analysis
This is because the models are complex, and most of them work as a black-box.
BIG-bench Machine Learning
Interpretable Machine Learning
+2
The Curious Case of Machine Learning In Malware Detection
Then, we discuss how malware detection in the wild present unique challenges for the current state-of-the-art machine learning techniques.
