CTQWformer fuses continuous-time quantum walks into a graph transformer and recurrent module to outperform standard GNNs and graph kernels on classification benchmarks.
Deep graph kernels
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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Correlation graphs built from windowed multi-channel sEMG plus a lightweight GNN yield 99% real-time accuracy on five hand gestures from eight subjects, exceeding three prior baselines.
citing papers explorer
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CTQWformer: A CTQW-based Transformer for Graph Classification
CTQWformer fuses continuous-time quantum walks into a graph transformer and recurrent module to outperform standard GNNs and graph kernels on classification benchmarks.
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A Graph Neural Network Model for Real-Time Gesture Recognition Based on sEMG Signals
Correlation graphs built from windowed multi-channel sEMG plus a lightweight GNN yield 99% real-time accuracy on five hand gestures from eight subjects, exceeding three prior baselines.