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Bayesian Active learning Anomaly detection.jl

Here we are using Bayesian CNNs to detect anomalies in time series data. We combine imporovements from different fields to produce one generalised active learning algorithm based on GASF, bayesian inference, deep image learning, batchBALD acquisition function.

Datasets

Here is a list of the several public industrial datasets on which we intend to benchmark out algorithm.

  1. SECOM: Semiconductor manufacturing process data.
  2. Data-driven prediction of battery cycle life before capacity degradation
  3. PHM DATA Challenge 18: Etching tool fault detection (PdM)

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If you want to support this project and help it gain visibility, please consider starring the repository. Doing well on such metrics may also help us secure academic funding in the future. Also, if you use this software as part of your research, I would appreciate that you include the following citation in your paper.

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