Research map / Optimization / Implicit bias and generalization
Neural Tangents: Fast and Easy Infinite Neural Networks in Python
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Increase the number of Monte Carlo samples | Neural Tangents Monte Carlo kernel estimation · WideResNet kernel convergence | distance to empirical kernel | Helped |
| Increase the network widening factor | Finite-channel WideResNet · WideResNet kernel convergence | distance to infinite-width kernel | Helped |
| Use a WideResNet rather than a convolutional or fully connected architecture | Infinite-width NNGP or NTK inference · CIFAR-10 | performance | Helped |
| Increase the training-set size | Infinite-width FC, convolutional, and WideResNet models · CIFAR-10 | performance | Helped |
| Distribute kernel computation across more accelerators | Neural Tangents analytic kernel computation · 21-layer ReLU network with global average pooling | time per sample | Helped |
| Increase batch size for blocked covariance computation | Neural Tangents analytic kernel computation · 21-layer ReLU network with global average pooling | performance | Helped |
Closest papers by meaning
- Fast Finite Width Neural Tangent Kernel (ICML 2022 · similarity 0.86)
- Neural Tangent Kernel: Convergence and Generalization in Neural Networks (NeurIPS 2018 · similarity 0.86)
- Finite-Width Neural Tangent Kernels from Feynman Diagrams (ICML 2026 · similarity 0.84)
- Gradient Descent in Neural Networks as Sequential Learning in Reproducing Kernel Banach Space (ICML 2023 · similarity 0.84)
- Optimized Random Features for the Neural Tangent Kernel (Student Abstract) (AAAI 2025 · similarity 0.82)
- The Challenges of the Nonlinear Regime for Physics-Informed Neural Networks (NeurIPS 2024 · similarity 0.81)
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