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Quantitative CLTs in Deep Neural Networks
We study the distribution of a fully connected neural network with random Gaussian weights and biases in which the hidden layer widths are proportional to a large constant $n$.
A Quantitative Functional Central Limit Theorem for Shallow Neural Networks
We prove a Quantitative Functional Central Limit Theorem for one-hidden-layer neural networks with generic activation function.
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