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Found 4 papers, 3 papers with code
LEAN-DMKDE: Quantum Latent Density Estimation for Anomaly Detection
This paper presents an anomaly detection model that combines the strong statistical foundation of density-estimation-based anomaly detection methods with the representation-learning ability of deep-learning models.
AD-DMKDE: Anomaly Detection through Density Matrices and Fourier Features
The prediction phase complexity of the proposed algorithm is constant relative to the training data size, and it performs well in data sets with different anomaly rates.
InQMAD: Incremental Quantum Measurement Anomaly Detection
State-of-the-art flow anomaly detection methods rely on fixed memory using hash functions or nearest neighbors that may not be able to constrain high-frequency values as in a moving average or remove seamless outliers and cannot be trained in an end-to-end deep learning architecture.
Risk Automatic Prediction for Social Economy Companies using Camels
Thus, the risk of a SEE in a future period can be predicted by a supervised machine learning method.
