Gadi Naveh on Muck Rack

Gadi Naveh

As seen in: Physical Review B
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Articles

Predicting the outputs of finite deep neural networks trained with noisy gradients

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By Agilent Laboratories, Petach Tikva, Gadi Naveh, Oded Ben David
| Physical Review B
Abstract A recent line of works studied wide deep neural networks (DNNs) by approximating them as Gaussian processes (GPs). A DNN trained with gradient flow was shown to map to a GP governed by the neural tangent kernel (NTK), whereas earlier works showed that a DNN with an i.i.d. prior over its weights maps to the so-called neural network Gaussian process (NNGP).
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