OutlierDetection.jl: A modular outlier detection ecosystem for the Julia programming language
원문 초록
OutlierDetection.jl is an open-source ecosystem for outlier detection in Julia. It provides a range of high-performance outlier detection algorithms implemented directly in Julia. In contrast to previous packages, our ecosystem enables the development highly-scalable outlier detection algorithms using a high-level programming language. Additionally, it provides a standardized, yet flexible, interface for future outlier detection algorithms and allows for model composition unseen in previous packages. Best practices such as unit testing, continuous integration, and code coverage reporting are enforced across the ecosystem. The most recent version of OutlierDetection.jl is available at https://github.com/OutlierDetectionJL/OutlierDetection.jl.
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Robust Spatiotemporal Epidemic Modeling with Integrated Adaptive Outlier Detection
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outliers
Importance Sampling for Nonlinear Models
Benign Samples Matter! Fine-tuning On Outlier Benign Samples Severely Breaks Safety
Adversarial Subspace Generation for Outlier Detection in High-Dimensional Data
Out-of-Distribution Detection on Graphs: A Survey
토론
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