Latent State Model

Latent state models aim to uncover hidden, underlying processes driving observed data by representing them as transitions between unobservable states. Current research focuses on applying these models, often using Hidden Markov Models (HMMs) or variations thereof, to diverse areas including neural network training dynamics, user behavior modeling, and even interpreting the internal structure of deep learning models. This work improves understanding of complex systems by providing interpretable representations of otherwise opaque processes, leading to better predictions and more effective interventions in various applications. The ability to perform counterfactual analysis within these dynamic models further enhances their utility for causal inference and decision-making.