CTDG-SSM introduces CTT-HiPPO, a Laplacian-polynomial projection of HiPPO, to create a parameter-efficient state-space formulation for continuous-time dynamic graphs that captures long-range spatio-temporal patterns.
Dyg-mamba: Continuous state space modeling on dynamic graphs.arXiv preprint arXiv:2408.06966, 2024a
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STM3 is a new multiscale Mamba mixture-of-experts model with graph causal networks and contrastive routing that reports state-of-the-art results on 10 long-term spatio-temporal forecasting benchmarks.
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Learning Long Range Spatio-Temporal Representations over Continuous Time Dynamic Graphs with State Space Models
CTDG-SSM introduces CTT-HiPPO, a Laplacian-polynomial projection of HiPPO, to create a parameter-efficient state-space formulation for continuous-time dynamic graphs that captures long-range spatio-temporal patterns.
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STM3: Mixture of Multiscale Mamba for Long-Term Spatio-Temporal Time-Series Prediction
STM3 is a new multiscale Mamba mixture-of-experts model with graph causal networks and contrastive routing that reports state-of-the-art results on 10 long-term spatio-temporal forecasting benchmarks.