6 citations · 7 across the 2 of their papers we have counts for
2 papers
cs.LG2021★ 6 cited
A self consistent theory of Gaussian Processes captures feature learning effects in finite CNNs
Gadi Naveh, Zohar Ringel
Deep neural networks (DNNs) in the infinite width/channel limit have received much attention recently, as they provide a clear analytical window to deep learning via mappings to Ga…
stat.ML2020★ 1 cited
Predicting the outputs of finite deep neural networks trained with noisy gradients
Gadi Naveh, Oded Ben-David, Haim Sompolinsky +1
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 governe…