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Adam or Gauss-Newton? A Comparative Study In Terms of Basis Alignment and SGD Noise
Preconditioned methods are central to deep learning optimization. Predominant approaches include computationally light diagonal preconditioners such as Adam, which rely on gradient statistics, and second-order methods such as Gauss-Newton (GN), which capture richer curvature information. Seeking the
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