近年来,空间状态模型(SSMs)作为一种基于注意力的序列建模架构的替代方案,由于其效率越来越受到欢迎。Mamba,一种选择性状态空间模型,它使用循环扫描和选择机制来控制序列的哪一部分可以流入隐藏状态。这种选择可以简单地解释为使用依赖于数据的状态转换机制。Mamba架构是专门为序列数据设计的,而图复杂的性质使直接将Mamba应...
Recently, State-Space Models (SSMs), exemplified by Mamba, have gained significant attention as a promising alternative due to their linear computational complexity. Another approach, neural memory Ordinary Differential Equations (nmODEs), exhibits similar principles and achieves good results. In this ...
2)提出了一种新的通用视觉骨干generic vision backbone网络- Vim——使用双向Mamba块bidirectional Mamba blocks来标记图像序列,并使用双向状态空间模型bidirectional state space models压缩视觉表示。 以前的工作 论文中提到:Some SSM-based methods, such as the linear state-space layers (LSSL), structuredstatespace...
We motivate our selection mechanism using intuition from synthetic tasks (Section 3.1), then explain how to incorporate this mechanism into state space models (Section 3.2). The resulting time-varying SSMs cannot use convolutions, presenting a technical challenge of how to compute them efficiently. ...
State space models (SSMs) offer a more efficient alternative to transformers. SSMs scale with linear complexity, making them significantly faster and more memory-efficient for long sequences. However, SSMs are limited in recalling information and often underperform compared to transformers, especially on...
状态空间模型(State Space Models) 状态空间模型(SSMs)通常被认为是将刺激 映射到响应 的线性时不变系统。从数学上讲,这些模型通常被构建为线性常微分方程(ODEs): ,其中 , 、 , 为状态大小,以及跳跃连接 。 离散化(Discretization) 没看懂,后来再看一遍。
2 State Space Models 3 Selective State Space Models and 3.1 Motivation: Selection as a Means of Compression 3.2 Improving SSMs with Selection 3.3 Efficient Implementation of Selective SSMs 3.4 A Simplified SSM Architecture 3.5 Properties of Selection Mechanisms ...
is standalone, and can be used for any sequence modeling problem, one does not use by default this formulation where we carry on the hidden state. The implementation is the same as the original JAX implementation and can be downloaded in zip format fromssms_event_cameras/RVT/models/s5.zip....
State-space models (SSMs) have emerged as an alternative to Transformers for audio modeling due to their high computational efficiency with long inputs. While recent efforts on Audio SSMs have reported encouraging results, two main limitations remain: First, in 10-second short audio tagging tasks,...
Recently, state-space models (SSMs) have emerged as a more efficient alternative, affording a linear inference complexity in the context size. This work explores the potential of SSMs for ICL-based equalization in cell-free massive MIMO systems. Results show that selective SSMs achieve comparable ...