DRL-STAF uses deep RL to predict observations and estimate discrete hidden states for multivariate hidden Markov processes, outperforming HMMs, deep learning models, and hybrids in experiments.
EM procedures using mean field-like approximations for Markov model-based image segmentation
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DRL-STAF: A Deep Reinforcement Learning Framework for State-Aware Forecasting of Complex Multivariate Hidden Markov Processes
DRL-STAF uses deep RL to predict observations and estimate discrete hidden states for multivariate hidden Markov processes, outperforming HMMs, deep learning models, and hybrids in experiments.