Nov 24, 2023 · In this paper, we investigate the long-term memory learning capabilities of state-space models (SSMs) from the perspective of parameterization.
Nov 22, 2023 · In this paper, we investigate the long-term memory learning capabilities of state-space models (SSMs) from the perspective of ...
Jun 5, 2024 · In this paper, we investigate the long-term memory learning capabilities of state-space models (SSMs) from the perspective of ...
May 1, 2024 · We prove that state-space models without any reparameterization exhibit a memory limitation similar to that of traditional RNNs: the target ...
Dec 5, 2023 · To this end, we introduce a class of reparameterization techniques for SSMs that effectively lift its memory limitations. Besides improving ...
StableSSM: Alleviating the Curse of Memory in State-space Models through Stable Reparameterization ... Improve Long-term Memory Learning Through Rescaling the ...
[2023_017] "StableSSM: Alleviating the Curse of Memory in State-space Models through Stable Reparameterization." Wang, Shida, and Qianxiao Li. arXiv ...
StableSSM: Alleviating the Curse of Memory in State-space Models through Stable Reparameterization. S Wang, Q Li. Proceedings of the 41 st International ...
Nov 24, 2023 · In this paper, we investigate the long-term memory learning capabilities of state-space models (SSMs) from the perspective of ...
Collection of papers/repos on state-space models. ICML 2024. StableSSM: Alleviating the Curse of Memory in State-space Models through Stable Reparameterization ...