Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian
Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation
G. Yang. (2019)cite arxiv:1902.04760Comment: tldr: A theoretical tool for understanding the behavior of large width randomly initialized neural networks for almost all deep learning architectures.
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