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arXiv Machine Learning By Natsuki Yoshino, Ren Uchida, Kazuki Matsumoto, Kohei Yatabe

LipSSM: Structurally Lipschitz-Bounded Cascaded State-Space Model via Metric Transfer between Consecutive SSM Layers

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LipSSM introduces a cascaded state‑space model that enforces Lipschitz continuity across layers by transferring metric information between consecutive SSM layers, thereby tightening the overall Lipschitz bound compared to traditional layer‑wise methods. This approach aims to preserve robustness while improving the expressive capacity of deep neural networks for modeling longer‑term dependencies. The architecture is both theoretically justified and empirically evaluated in the paper.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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