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Linear Dynamics-embedded Neural Network for Long-Sequence Modeling
The trade-off between performance and computational efficiency in long-sequence modeling becomes a bottleneck for existing models.
Spatiotemporal Observer Design for Predictive Learning of High-Dimensional Data
Although deep learning-based methods have shown great success in spatiotemporal predictive learning, the framework of those models is designed mainly by intuition.
Multiscale Fusion for Abnormality Detection and Localization of Distributed Parameter Systems
Numerous industrial thermal processes and fluid processes can be described by distributed parameter systems (DPSs), wherein many process parameters and variables vary in space and time.
Fidelity-based Probabilistic Q-learning for Control of Quantum Systems
In this paper, a fidelity-based probabilistic Q-learning (FPQL) approach is presented to naturally solve this problem and applied for learning control of quantum systems.
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