A self consistent theory of Gaussian Processes captures feature learning effects in finite CNNsDownload PDF

Published: 09 Nov 2021, Last Modified: 05 May 2023NeurIPS 2021 PosterReaders: Everyone
Keywords: Statistical mechanics, Mean field theory, Gaussian Processes, feature learning, finite DNNs
TL;DR: We cast learning in finite CNNs as Gaussian process regression with a non-centred prior, yielding analytical predictions for strong feature learning effects.
Abstract: 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 Gaussian Processes (GPs). Despite its theoretical appeal, this viewpoint lacks a crucial ingredient of deep learning in finite DNNs, laying at the heart of their success --- \textit{feature learning}. Here we consider DNNs trained with noisy gradient descent on a large training set and derive a self-consistent Gaussian Process theory accounting for \textit{strong} finite-DNN and feature learning effects. Applying this to a toy model of a two-layer linear convolutional neural network (CNN) shows good agreement with experiments. We further identify, both analytically and numerically, a sharp transition between a feature learning regime and a lazy learning regime in this model. Strong finite-DNN effects are also derived for a non-linear two-layer fully connected network. We have numerical evidence demonstrating that the assumptions required for our theory hold true in more realistic settings (Myrtle5 CNN trained on CIFAR-10). Our self-consistent theory provides a rich and versatile analytical framework for studying strong finite-DNN effects, most notably - feature learning.
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