Bidirectional Mamba state-space model for anomalous diffusion
On the map: State-space sequence models (S4, Mamba) → Video, motion and generative Mamba models
Abstract (arXiv)
Characterizing anomalous diffusion is crucial in order to understand the evolution of complex stochastic systems, from molecular interactions to cellular dynamics. In this work, we characterize the performances regarding such a task of Bi-Mamba, a novel state-space deep-learning architecture articulated with a bidirectional scan mechanism. Our implementation is tested on the AnDi-2 challenge datasets among others. Designed for regression tasks, the Bi-Mamba architecture infers efficiently the effective diffusion coefficient and anomalous exponent from single, short trajectories. As such, our results indicate the potential practical use of the Bi-Mamba architecture for anomalousdiffusion characterization.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Video, motion and generative Mamba models, one of 10 directions in State-space sequence models (S4, Mamba). Builds on 1 of its 5 references that are in the map. First posted on arXiv 10 Dec 2024.
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- 2024Bi-Mamba+: Bidirectional Mamba for Time Series Forecastingcited by 14
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- 2021Extreme Learning Machine for the Characterization of Anomalous Diffusion from Single Trajectories (AnDi-ELM)