AISLEX: Approximate individual sample learning entropy with JAX

We present AISLEX, an online anomaly detection module based on the Learning Entropy algorithm, a novel machine learning-based information measure that quantifies the learning effort of neural networks. AISLEX detects anomalous data samples when the learning entropy value is high. The module is desig...

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Bibliographic Details
Published inSoftwareX Vol. 28; p. 101915
Main Authors Budik, Ondrej, Novak, Milan, Sobieczky, Florian, Bukovsky, Ivo
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.12.2024
Subjects
Anomaly detection
JAX
Learning entropy
Machine learning weight evaluation
JAX
Machine learning weight evaluation
Learning entropy
Anomaly detection
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Summary:We present AISLEX, an online anomaly detection module based on the Learning Entropy algorithm, a novel machine learning-based information measure that quantifies the learning effort of neural networks. AISLEX detects anomalous data samples when the learning entropy value is high. The module is designed to be readily usable, with both NumPy and JAX backends, making it suitable for various application fields. The NumPy backend is optimized for devices running Python3, prioritizing limited memory and CPU usage. In contrast, the JAX backend is optimized for fast execution on CPUs, GPUs, and TPUs but requires more computational resources. AISLEX also provides extensive implementation examples in Jupyter notebooks, utilizing in-parameter-linear-nonlinear neural architectures selected for their low data requirements, computational simplicity, convergence analyzability, and dynamical stability.
ISSN:2352-7110
2352-7110
DOI:10.1016/j.softx.2024.101915