Unverified50% confidenceFactExact time
SSU-LSF claims to be the first machine unlearning framework specifically designed for geoscience Mamba-class state space models.
1
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50%
Confidence
Long-term
Relevance
10/5/2026
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VerifiedMamba 等基于状态空间模型（SSM）的架构以线性复杂度实现序列建模，更适合实时推理71% similarVerifiedState space models (SSMs) such as Mamba abandon the attention mechanism and use recurrent structures to achieve linear time complexity sequence modeling.68% similarVerifiedState space models represented by Mamba compress historical information by maintaining a fixed-size hidden state, with both time and space complexity of O(1) during inference.67% similarUnverifiedSince 2024, Mamba2 has further unified the theoretical framework of state space models and attention mechanisms, and hybrid architectures such as Jamba interleave Mamba layers with attention layers67% similarUnverifiedSSU-LSF uses dedicated EKFac influence functions for the Mamba state matrix to quantify the contribution of events to parameters via closed-form matrix exponential gradients.66% similar
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