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stat.ML2026
High-Dimensional Limit of Stochastic Gradient Flow via Dynamical Mean-Field Theory
Sota Nishiyama, Masaaki Imaizumi
Modern machine learning models are typically trained via multi-pass stochastic gradient descent (SGD) with small batch sizes, and understanding their dynamics in high dimensions is…
stat.ML2025
Precise Dynamics of Diagonal Linear Networks: A Unifying Analysis by Dynamical Mean-Field Theory
Sota Nishiyama, Masaaki Imaizumi
Diagonal linear networks (DLNs) are a tractable model that captures several nontrivial behaviors in neural network training, such as initialization-dependent solutions and incremen…