  A statistical ensemble of neural networks can be described in terms of a
quantum field theory (NN-QFT correspondence). The infinite-width limit is
mapped to a free field theory, while finite N corrections are mapped to
interactions. After reviewing the correspondence, we will describe how to
implement renormalization in this context and discuss preliminary numerical
results for translation-invariant kernels. A major outcome is that changing the
standard deviation of the neural network weight distribution corresponds to a
renormalization flow in the space of networks.
