GAMMA-Net combines Graph Attention Networks and multi-axis Mamba to outperform prior models in long-horizon traffic forecasting, with up to 16.25% lower MAE on benchmarks like METR-LA and PEMS datasets.
Stg-mamba: Spatial-temporal graph learning via selective state space model
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TopoMamSurv introduces topology-aware ordering and bidirectional Mamba with GCN for efficient WSI graph survival analysis, claiming performance gains on five TCGA datasets.
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.
A literature survey of State Space Model methods applied to remote sensing tasks, architectures, and challenges since their introduction to the field.
The paper consolidates existing research on Mamba models, their architecture variants, adaptations to different data modalities, and applications across domains.
citing papers explorer
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GAMMA-Net: Adaptive Long-Horizon Traffic Spatio-Temporal Forecasting Model based on Interleaved Graph Attention and Multi-Axis Mamba
GAMMA-Net combines Graph Attention Networks and multi-axis Mamba to outperform prior models in long-horizon traffic forecasting, with up to 16.25% lower MAE on benchmarks like METR-LA and PEMS datasets.
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Graph Mamba Survival Analysis Based on Topology-Aware ordering
TopoMamSurv introduces topology-aware ordering and bidirectional Mamba with GCN for efficient WSI graph survival analysis, claiming performance gains on five TCGA datasets.
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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.
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State Space Models Meet Remote Sensing: A Survey
A literature survey of State Space Model methods applied to remote sensing tasks, architectures, and challenges since their introduction to the field.
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A Survey of Mamba
The paper consolidates existing research on Mamba models, their architecture variants, adaptations to different data modalities, and applications across domains.