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Jul 9, 2002 · In this paper, I describe two hierarchical abstraction mechanisms for simplifying the (estimation) learning and (control) optimization of ...
Abstract. Probabilistic finite state machines have become a popular modeling tool for representing sequential processes, ranging from images.
Sridhar Mahadevan, "Spatiotemporal Abstraction of Stochastic Sequential Processes" , Symposium on Abstraction, Reformulation, and Approximation (SARA) ...
A stochastic dependency neural estimator is developed for spatiotemporal dynamics. GNNs are employed to capture spatiotemporal features in the time-space ...
A spatio-temporal analysis of events is concerned with identifying high-intensity spots and their evolution over space and time, and while traditional cluster ...
Jan 17, 2024 · It automatically decomposes the given task into smaller, more manageable subtasks, and hence enables sparse decision-making and focused ...
Oct 14, 2022 · Learning and replaying spatiotemporal sequences are fundamental computations performed by the brain and specifically the neocortex.
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We propose a spatiotemporal learning-based stochastic model predictive control algorithm to study the stochastic optimal control problem with dynamically ...
A spatio-temporal point process is a stochastic process composed of events with time and space that occur over a domain [35]. These spatio-temporal events are ...
The report has three parts: construction of stochastic models of spatially extended processes (Section 3), methods for estimation of model parameters from.