Deep neural networks can be represented as Bayesian hierarchical models built from neuron-defined Gibbs distributions, yielding a new explanation of regularization and generalization.
Stochastic gradient descent performs variational inference, converges to limit cycles for deep networks
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A Probabilistic Representation of Deep Learning
Deep neural networks can be represented as Bayesian hierarchical models built from neuron-defined Gibbs distributions, yielding a new explanation of regularization and generalization.