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The future is log-Gaussian: ResNets and their infinite-depth-and-width limit at initialization

NeurIPS 2021 · Architectures and efficiency › RNNs and normalization · approach: Theory of depth and width · 2 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
use Balanced ResNets instead of Vanilla ResNetsReLU ResNet at initialization · d/n=1variance contribution of interlayer correlation: e^{5.5} \approx 250 -> e^{2.5} \approx 10Helped
log-Gaussian limit instead of Gaussian infinite-width limitfinite ResNet output at initialization · Monte Carlo simulationsagreement with finite-network mean and varianceHelped

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