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Showing 1 to 7 of 7 for “"tensor train decomposition"”.
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A tensor-train-decomposition-based algorithm for high-dimensional pursuit-evasion games
… under certain circumstances by utilizing tensor-train (TT) decomposition. By using this intuition, a new algorithm for solving high dimensional pursuit-evasion problems called Best-Response Tensor-Train-decomposition-based Value Iteration (BR-TT-VI) was developed. BR-TT-VI builds on …
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Automated synthesis of low-rank stochastic dynamical systems using the tensor-train decomposition
… behavior. The tight coupling of physical constraints and computation that typically characterize cyber-physical systems make them extremely complex, resulting in unexpected failure modes. Furthermore, disturbances in the environment and uncertainties in the physical model require these systems …
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Agile quadrotor maneuvering using tensor-decomposition-based globally optimal control and onboard visual-inertial estimation
… resulting optimization problem is solved using tensor-train-decomposition-based compressed continuous computation techniques. The platform's capabilities and the potential of these types of controllers are demonstrated in both simulation studies and in experiments.
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Functional analysis of low grade glioma genetic variants using statistics and physics-inspired deep learning methods
… interpretation methods. Finally, we applied tensor train decomposition (TT-decomposition) to neural network parameter reduction and demonstrated that the reduced convolutional neural network performed well. This work helps understand the molecular mechanisms underlying genetic risk factors of …
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Continuous low-rank tensor decompositions, with applications to stochastic optimal control and data assimilation
… framework tightly integrates two emerging areas: tensor decompositions and continuous computation. Tensor decompositions are able to effectively compress and operate with low-rank multidimensional arrays. Continuous computation is a paradigm for computing with functions instead of arrays, and it …
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Tensors and Stochastic Automata Networks with Application to Chemical Kinetics
… simulations to more sophisticated higher-order tensors and stochastic automata networks. Many revolve around solving the chemical master equation that arises in the modeling of the underlying biochemical kinetics. Traditionally, the chemical master equation models states consisting of the …