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explainable-deepneuralnetwork

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This repository introduces different Explainable AI approaches and demonstrates how they can be implemented with PyTorch and torchvision. Used approaches are Class Activation Mappings, LIMA and SHapley Additive exPlanations.

  • Updated Jul 1, 2022
  • Jupyter Notebook

Sensor selection for process monitoring based on deciphering acoustic emissions from different dynamics of the Laser Powder Bed Fusion process using Empirical Mode Decompositions and Interpretable Machine Learning

  • Updated Sep 10, 2023
  • Python

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