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detection and monitoring, invariant and distributionally robust representation learning algorithms, and deployment-time calibration with uncertainty quantification using approaches such as conformal
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combined to form a continuously evolving representation of an interaction. Rather than simply classifying the current human state, the model should be able to anticipate possible future states and use these
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refers to contextual information that serves as a representation or substitute for actual real-world data. This project will draw inspiration from research into how spatial awareness and context
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pollinate. Bayesian networks (BNs), and other probabilistic graphical models, can provide a visual representation of the underlying structure of a complex system by representing domain experts’ beliefs
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pollinate. Bayesian networks (BNs), and other probabilistic graphical models, can provide a visual representation of the underlying structure of a complex system by representing domain experts’ beliefs