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Universal Consistency of Wide and Deep ReLU Neural Networks and Minimax Optimal Convergence Rates for Kolmogorov-Donoho Optimal Function Classes
In this paper, we prove the universal consistency of wide and deep ReLU neural network classifiers trained on the logistic loss.
On Excess Risk Convergence Rates of Neural Network Classifiers
The recent success of neural networks in pattern recognition and classification problems suggests that neural networks possess qualities distinct from other more classical classifiers such as SVMs or boosting classifiers.
Generalization of Overparametrized Deep Neural Network Under Noisy Observations
We study the generalization properties of the overparameterized deep neural network (DNN) with Rectified Linear Unit (ReLU) activations.
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