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A statistical mechanics framework for Bayesian deep neural networks beyond the infinite-width limit - Nature Machine Intelligence

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A statistical mechanics framework for Bayesian deep neural networks beyond the infinite-width limit - Nature Machine Intelligence

Despite the practical success of deep neural networks, a comprehensive theoretical framework that can predict practically relevant scores, such as the test accuracy, from knowledge of the training data is currently lacking. Huge simplifications arise in the infinite-width limit, in which the number of units Nℓ in each hidden layer (ℓ = 1, …, L, where L is the depth of the network) far exceeds the number P of training examples.

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A statistical mechanics framework for Bayesian deep neural networks beyond the infinite-width limit - Nature Machine Intelligence

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A statistical mechanics framework for Bayesian deep neural networks beyond the infinite-width limit - Nature Machine Intelligence

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