Non-perturbative renormalization for the neural network-QFT correspondence
ABSTRACT :
This seminar is a part of the activities of Center for Quantum Information Theory of Matter and Spacetime of IIT Madras
In a recent work, Halverson, Maiti and Stoner proposed a description of neural networks in terms of an effective field theory (dubbed NN-QFT correspondence). The infinite-width limit is mapped to a free field theory while finite N corrections are taken into account by interactions. In this talk, after reviewing the correspondence, I will derive non-perturbative renormalization group equations. An important difference with the usual analysis is that the effective (IR) 2-point function is known, while the microscopic (UV) 2-point function is not, which requires setting the problem with care. Finally, I will discuss preliminary numerical results for translation-invariant kernels. A major result is that changing the standard deviation of the neural network weight distribution can be interpreted as a renormalization flow in the space of networks.