The problem of jointly detecting multiple objects and estimating their states from image observations is formulated in a Bayesian framework by modeling the collection of states as ...
Ba-Ngu Vo, Ba-Tuong Vo, Nam-Trung Pham, David Sute...
We propose a new neural network architecture, called Simple Recurrent Temporal-Difference Networks (SR-TDNs), that learns to predict future observations in partially observable en...
This paper presents a methodology for the real-time extraction of readers’ emotional state from documents as well as the representation of emotionally annotated documents into a...
Abstract: Bisimulations in general are a powerful concept to minimize large finite state systems regarding some well-defined observational behavior. In contrast to strong bisimul...
This paper extends basic software-testing theory to software components and adds explicit state to the theory. The resulting theory e enough to abstractly model the construction o...