Feature learning as alignment: a structural property of gradient descent in non-linear neural networks

• URL: http://arxiv.org/abs/2402.05271v3
• Date: Mon, 24 Jun 2024 15:55:34 GMT
• Title: Feature learning as alignment: a structural property of gradient descent in non-linear neural networks
• Authors: Daniel Beaglehole, Ioannis Mitliagkas, Atish Agarwala,
• Abstract summary: We show that neural networks extract statistics from input-label pairs through feature learning. We establish that the alignment is driven by the interaction of weight changes induced by SGD with the pre-activation features. We introduce a simple optimization rule that dramatically increases the NFA correlations at any given layer and improves the quality of features learned.
• Score: 13.032185349152492
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Understanding the mechanisms through which neural networks extract statistics from input-label pairs through feature learning is one of the most important unsolved problems in supervised learning. Prior works demonstrated that the gram matrices of the weights (the neural feature matrices, NFM) and the average gradient outer products (AGOP) become correlated during training, in a statement known as the neural feature ansatz (NFA). Through the NFA, the authors introduce mapping with the AGOP as a general mechanism for neural feature learning. However, these works do not provide a theoretical explanation for this correlation or its origins. In this work, we further clarify the nature of this correlation, and explain its emergence. We show that this correlation is equivalent to alignment between the left singular structure of the weight matrices and the newly defined pre-activation tangent features at each layer. We further establish that the alignment is driven by the interaction of weight changes induced by SGD with the pre-activation features, and analyze the resulting dynamics analytically at early times in terms of simple statistics of the inputs and labels. Finally, motivated by the observation that the NFA is driven by this centered correlation, we introduce a simple optimization rule that dramatically increases the NFA correlations at any given layer and improves the quality of features learned.

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