mango
neural-tangents
| mango | neural-tangents | |
|---|---|---|
| - | 4 | |
| 310 | 2,221 | |
| 1.0% | 0.5% | |
| 5.8 | 7.6 | |
| about 2 months ago | 2 months ago | |
| Jupyter Notebook | Jupyter Notebook | |
| Apache License 2.0 | Apache License 2.0 |
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mango
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neural-tangents
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Any Deep ReLU Network Is Shallow
is used to capture the power of a fully-trained deep net of infinite width.
https://openreview.net/pdf?id=rkl4aESeUH, https://github.com/google/neural-tangents
> It has long been known that a single-layer fully-connected neural network with an i.i.d. prior over its parameters is equivalent to a Gaussian process (GP), in the limit of infinite network width.
https://arxiv.org/abs/1711.00165
And of course, one needs to look back at SVMs applying a kernel function and separating with a line, which looks a lot like an ANN with a single hidden layer followed by a linear mapping.
https://stats.stackexchange.com/questions/238635/kernel-meth...
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[R] Training Machine Learning Models More Efficiently with Dataset Distillation
Code for https://arxiv.org/abs/2011.00050 found: https://github.com/google/neural-tangents
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[D] Relationship Between Kernels, Neural Networks and Gaussian Process
I saw that you asked about neural tangent kernels (NTK) in another post yesterday -- be aware that what you're referencing in the present post are "neural network gaussian processes" (NNGP), which is distinct from NTK! The README of https://github.com/google/neural-tangents should help lift confusion. (I also took the term NNGP from there.)
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[D] neural tangent kernel
It's true! There have been dozens of papers published on this topic, some of which are listed here: https://github.com/google/neural-tangents#references
What are some alternatives?
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