A novel latent robust world model (LRWM) with double latent features is elaborated based on probabilistic graphical model (PGM) to learn the transition of the USV navigation and predict future latent ...

A novel semi-supervised framework combining contrastive learning with hierarchical probabilistic graphical models for remote sensing with limited labeled data. Our approach enhances CRFNet by learning ...

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical ...