A hidden Markov model fit to aligned per-window classifier weights recovers temporal states, and models transfer better within those states than across boundaries.
Komal Florio, Valerio Basile, Marco Polignano, Pierpaolo Basile, and Viviana Patti
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Latent States in Neural Networks: Recovering the Temporal Structure of Drifting Data from Model Weights
A hidden Markov model fit to aligned per-window classifier weights recovers temporal states, and models transfer better within those states than across boundaries.