Research map / Architectures and efficiency / RNNs and normalization
NAIS-Net: Stable Deep Networks from Non-Autonomous Differential Equations
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| unroll each NAIS-Net block 10 times instead of once (ablation) | NAIS-Net · CIFAR-100 test | accuracy: 65.00 ± 0.52 -> 66.07 ± 0.24 | Helped |
| unroll each NAIS-Net block 10 times instead of once (ablation) | NAIS-Net · CIFAR-10 test | accuracy: 91.24±0.10 -> 91.25±0.46 | No clear effect |
| replace ResNet with NAIS-Net (stable, non-autonomous, shared weights) | ResNet · CIFAR-10 | generalization gap: reduced by 33% | Helped |
| replace ResNet with NAIS-Net10 | ResNet · CIFAR-100 test | accuracy: 66.34 ± 0.82 -> 66.07 ± 0.24 | No clear effect |
| use NAIS-Net instead of shared-weight ResNet without BatchNorm (ablation) | ResNet-SH · MNIST | test accuracy: 95.86% -> 97.28% | Helped |
| add batch normalization to shared-weight ResNet (ablation) | ResNet-SH · MNIST | test accuracy: 95.86% -> 96.69% | Helped |
| enforce stability without non-autonomy (ResNet-SH-STABLE) (ablation) | shared-weight ResNet · MNIST | ability to learn | Hurt |
Closest papers by meaning
- Zero Stability Well Predicts Performance of Convolutional Neural Networks (AAAI 2022 · similarity 0.87)
- Almost Surely Stable Deep Dynamics (NeurIPS 2020 · similarity 0.86)
- Improving the Robustness of Deep Neural Networks via Stability Training (CVPR 2016 · similarity 0.86)
- Stable Gradients for Stable Learning at Scale in Deep Reinforcement Learning (NeurIPS 2025 · similarity 0.85)
- One-Time Soft Alignment Enables Resilient Learning without Weight Transport (NeurIPS 2026 · similarity 0.85)
- A Minimalist Example of Edge-of-Stability and Progressive Sharpening (NeurIPS 2025 · similarity 0.83)
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