Research map / Architectures and efficiency / RNNs and normalization
The future is log-Gaussian: ResNets and their infinite-depth-and-width limit at initialization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use Balanced ResNets instead of Vanilla ResNets | ReLU ResNet at initialization · d/n=1 | variance contribution of interlayer correlation: e^{5.5} \approx 250 -> e^{2.5} \approx 10 | Helped |
| log-Gaussian limit instead of Gaussian infinite-width limit | finite ResNet output at initialization · Monte Carlo simulations | agreement with finite-network mean and variance | Helped |
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- Neural signature kernels as infinite-width-depth-limits of controlled ResNets (ICML 2023 · similarity 0.87)
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- Feature Learning Dynamics in Infinite-Depth Neural Networks (NeurIPS 2026 · similarity 0.85)
- Quantitative convergence of trained neural networks to Gaussian processes (NeurIPS 2025 · similarity 0.85)
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