arXiv Statistics ML
Extending SSMs with the Exponentially Weighted Signature
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The Expressive Limits of Diagonal SSMs for State-Tracking
arXiv:2603. 01959v2 Announce Type: replace Abstract: State-Space Models (SSMs) have recently been shown to achieve strong empirical performance on a variety of long-range sequence modeling tasks while remaining efficient and highly-parallelizable.
arXiv Machine Learning
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Lag Operator SSMs: A Geometric Framework for Structured State Space Modeling
arXiv:2512. 18965v2 Announce Type: replace Abstract: Structured State Space Models (SSMs), which are at the heart of the recently popular Mamba architecture, are powerful tools for sequence modeling.
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A Simple State Space Model Excels at Multivariate Time Series Classification
arXiv:2605. 27406v2 Announce Type: replace Abstract: Structured state space models (SSMs) have recently emerged as a promising foundation for sequence modeling, with Mamba-based architectures demonstrating strong performance through input-dependent state transitions, albeit at considerable complexity.
arXiv Machine Learning
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ALPHABET: A Laplace-Pole History Aggregator with Banked Exponential Transport
arXiv:2608.24051v1 Announce Type: new Abstract: Can a sequence model remain competitive with only a few thousand parameters and an explicitly auditable prediction interface? We introduce ALPHABET, a...
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Online Signature Verification Using Augmented Path Signature and T-Mamba
The paper introduces a new online signature verification framework that combines the augmented path signature (APS) descriptor with a T-Mamba model. APS applies time and basepoint augmentations followed by sliding-window path signatures, capturing geometric structures and nonlinear inter-channel interactions. T-Mamba, a hybrid of two temporal convolutional network blocks and a time-scanning Mamba, learns both local temporal patterns and global long-range dependencies, achieving state‑of‑the‑art equal error rates on three public benchmark datasets.
arXiv AI
2d ago
Reshape and Recur: Improving SSMs with Input Reshaping and Depth Recurrence
The paper proposes two extensions to State Space Models (SSMs) to reduce memory usage and improve performance. First, it introduces depth recurrence, allowing a looped SSM with fewer parameters to match the performance of a larger, non-recurrent model. Second, it advocates using a fixed time granularity across tasks by reshaping input sequences, which enhances how information is presented to the model. Both techniques consistently benefit four representative SSM architectures (LRU, S5, LinOSS, LrcSSM).