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On the existence of infinitely many realization functions of non-global local minima for artificial neural networks with ReLU activation
Gradient descent (GD) type optimization schemes are standard tools for training fully connected feedforward artificial neural networks (ANNs) with rectified linear unit (ReLU) activation and can be considered as temporal discretizations of solutions of gradient flow (GF) differential equations. It i
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