I. Katerynchuk

Dynamic Regime Maps of Neural Network Training Under the Adam Optimizer: An Observable-Based Empirical Analysis

An observable-based empirical regime-mapping framework for analysing neural-network training under the Adam optimizer in the two-dimensional hyperparameter space defined by the exponential decay coefficients of the first and second gradient moments, and offers a complementary observable-based perspective on the complex...