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This paper investigates parallel-in-time algorithms for training recurrent neural networks in dynamical systems reconstruction, proposing GTF-DEER that enables stable learning over long sequences and improves reconstruction accuracy.
This paper introduces Gated QKAN-FWP, a scalable quantum-inspired sequence learning framework that combines Fast Weight Programmers with Kolmogorov-Arnold Networks using single-qubit data re-uploading circuits.
EEG-tGAT is a temporally augmented Graph Attention Network that improves affordance classification from interaction sequences by incorporating temporal attention and dropout mechanisms. The model enhances GATv2 for sequential data where temporal dimensions are semantically non-uniform.