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A unifying review of deep and shallow anomaly detection
Deep learning approaches to anomaly detection (AD) have recently improved the state of
the art in detection performance on complex data sets, such as large collections of images or …
the art in detection performance on complex data sets, such as large collections of images or …
Deep semi-supervised anomaly detection
Deep approaches to anomaly detection have recently shown promising results over shallow
methods on large and complex datasets. Typically anomaly detection is treated as an …
methods on large and complex datasets. Typically anomaly detection is treated as an …
Lidar degradation quantification for autonomous driving in rain
Autonomous driving in rainy conditions remains a big challenge. One of the issues is sensor
degradation. LiDAR is commonly used in autonomous driving systems to perceive and …
degradation. LiDAR is commonly used in autonomous driving systems to perceive and …
Nng-mix: Improving semi-supervised anomaly detection with pseudo-anomaly generation
Anomaly detection (AD) is essential in identifying rare and often critical events in complex
systems, finding applications in fields such as network intrusion detection, financial fraud …
systems, finding applications in fields such as network intrusion detection, financial fraud …
Monk outlier-robust mean embedding estimation by median-of-means
Mean embeddings provide an extremely flexible and powerful tool in machine learning and
statistics to represent probability distributions and define a semi-metric (MMD, maximum …
statistics to represent probability distributions and define a semi-metric (MMD, maximum …
Robust kernel density estimation with median-of-means principle
In this paper, we introduce a robust non-parametric density estimator combining the popular
Kernel Density Estimation method and the Median-of-Means principle (MoM-KDE). This …
Kernel Density Estimation method and the Median-of-Means principle (MoM-KDE). This …
M-estimators of location for functional data
M-estimators of location are widely used robust estimators of the center of univariate or
multivariate real-valued data. This paper aims to study M-estimates of location in the …
multivariate real-valued data. This paper aims to study M-estimates of location in the …
Graph neural network based abnormal perception information reconstruction and robust autonomous navigation
Z Zhang, Z Liu, Y Miao, X Ma - Robotic Intelligence and Automation, 2024 - emerald.com
Purpose This paper aims to develop a robust navigation enhancement framework to handle
one of the most urgent needs for real applications of autonomous vehicles nowadays, as …
one of the most urgent needs for real applications of autonomous vehicles nowadays, as …
M-estimates of location for the robust central tendency of fuzzy data
The Aumann-type mean has been shown to possess valuable properties as a measure of
the location or central tendency of fuzzy data associated with a random experiment …
the location or central tendency of fuzzy data associated with a random experiment …
Beyond smoothness: Incorporating low-rank analysis into nonparametric density estimation
The construction and theoretical analysis of the most popular universally consistent
nonparametric density estimators hinge on one functional property: smoothness. In this …
nonparametric density estimators hinge on one functional property: smoothness. In this …