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Found 4 papers, 1 papers with code
Wilsonian Renormalization of Neural Network Gaussian Processes
We systematically integrate out the unlearnable modes of the GP kernel, thereby obtaining an RG flow of the Gaussian Process in which the data plays the role of the energy scale.
Criticality versus uniformity in deep neural networks
In this work, we explore the effect of saturation of the tanh activation function along the edge of chaos.
The edge of chaos: quantum field theory and deep neural networks
We explicitly construct the quantum field theory corresponding to a general class of deep neural networks encompassing both recurrent and feedforward architectures.
Towards quantifying information flows: relative entropy in deep neural networks and the renormalization group
In particular, we quantify the flow of information by explicitly computing the relative entropy or Kullback-Leibler divergence in both the one- and two-dimensional Ising models under decimation RG, as well as in a feedforward neural network as a function of depth.
