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Norm-based Generalization Bounds for Compositionally Sparse Neural Networks
In this paper, we investigate the Rademacher complexity of deep sparse neural networks, where each neuron receives a small number of inputs.
On the Implicit Bias Towards Minimal Depth of Deep Neural Networks
Finally, we empirically show that the effective depth of a trained neural network monotonically increases when increasing the number of random labels in data.
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