RGNet implements renormalization-group-style hierarchical coarse-graining inside a neural network to produce multi-scale representations that improve fault prediction on the imbalanced AI4I dataset.
Convergence of Adam for non -convex objectives: relaxed hyperparameters and non -ergodic case,
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Neural Network Implementation of the Renormalization Group for Fault Diagnosis with Class Imbalance
RGNet implements renormalization-group-style hierarchical coarse-graining inside a neural network to produce multi-scale representations that improve fault prediction on the imbalanced AI4I dataset.