Researchers have precisely characterized how feature learning in neural networks reshapes the function space during gradient descent training. Their analysis, conducted in a high-dimensional proportional regime, shows that after a large gradient step, the feature distribution approximates a target-dependent spiked Gaussian covariance. This process induces a data-adaptive kernel that modifies the function space's spectral structure, selectively amplifying directions aligned with the target signal. AI
影响 Provides a theoretical framework for understanding how neural networks learn features, potentially guiding future model development.
排序理由 The cluster contains an academic paper detailing a theoretical analysis of neural network training dynamics.
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