Scalar embeddings of neural network training trajectories treated as temporal networks preserve main dynamical features including Lyapunov exponents, enable definition of a characteristic decorrelation time, and show asymptotic state spacings compatible with a skew lognormal distribution.
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Scalar Representations of Neural Network Training Dynamics
Scalar embeddings of neural network training trajectories treated as temporal networks preserve main dynamical features including Lyapunov exponents, enable definition of a characteristic decorrelation time, and show asymptotic state spacings compatible with a skew lognormal distribution.