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This work presents a framework for experiment with autoencoders.

This work presents a novel approach for unsupervised anomaly detection based on word embeddings using time series data represented as text.

This work presents a model of the running resistance of a locomotive powered by liquefied natural gas.

This work presents an informed weak supervision methodology for learning the deterioration of a battery from charge and discharge data.

This work presents a novel recurrent VAE-based framework for the assessment of Engine Health Monitoring (EHM) data in aircraft.

This work presents a graphical analysis of the progression of atrial arrhythmia through an ensemble of Generative Adversarial Network Discriminators.