A variational quantum autoencoder detects anomalies in brain MRI by scoring resistance to compression, reporting slice-level ROC-AUC of 0.95 and outperforming classical autoencoders and PCA on public datasets.
Variational autoencoder based synthetic data generation for imbalanced learning,
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VAEs generate synthetic malware to augment datasets, yielding reported gains in accuracy, precision, recall, and F1 for three ML classifiers.
citing papers explorer
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Compression-Driven Anomaly Detection in Brain MRI Using an Interpretable Quantum Autoencoder
A variational quantum autoencoder detects anomalies in brain MRI by scoring resistance to compression, reporting slice-level ROC-AUC of 0.95 and outperforming classical autoencoders and PCA on public datasets.
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Enhancing Malware Detection with Generative AI: Using Variational Autoencoders to Boost Machine Learning Classifiers' Performance
VAEs generate synthetic malware to augment datasets, yielding reported gains in accuracy, precision, recall, and F1 for three ML classifiers.