原文地址：https://pub.towardsai.net/understanding-mamba-and-selective-state-space-models-ssms-1519c...
code:https://github.com/state-spaces/mamba/tree/main Selective State Space Models:让SSM的计算和输入相关 Ecient Implementation of Selective SSMs:加速算法计算 A Simplied SSM Architecture:使用提出的算法,构造了一个block Selective State Space Models 为了有选择性的压缩信息,应该让B、C依赖于输入的参数 SSM...
我们的 Mamba 语言模型与类似规模的 Transformer 相比,具有 5 倍的生成吞吐量,而且 Mamba-3B 的质量与两倍于其规模的 Transformer 相当(例如,与 Pythia-3B 相比,常识推理的平均值高出 4 分,甚至超过 Pythia-7B)。 Section 2 State Space Models 状态空间模型 结构化状态空间序列模型(Structured state space sequen...
State Space Models (SSMs) have become serious contenders in the field of sequential modeling, challenging the dominance of Transformers. At the same time, Mixture of Experts (MoE) has significantly improved Transformer-based Large Language Models, including recent state-of-the-art open models. We ...
Recently, State Space Models (SSMs), and more specifically Selective State Space Models, with efficient hardware-aware implementation, have shown promising potential for long sequence modeling. Motivated by the success of SSMs, we present MambaMixer, a new architecture with data-dependent weights that...
In contrast, modern Selective State Space Models (SSSMs) present a new approach which treat STG Network as a system, and meticulously explore the STG system's dynamic state evolution across temporal dimension. In this work, we introduce Spatial-Temporal Graph Mamba (STG-Mamba) as the first ...
Inspired by the fact that humans count objects in high-resolution images by sequential scanning, we explore the potential of handling plant counting tasks via state space models (SSMs) for generating counting results. In this paper, we propose a new counting approach named CountMamba that ...
In this report, we identify the inability of these models to perform content-based reasoning as a key weakness and focus on Mamba, a novel neural network architecture that integrates selective structured state space models (SSMs) to address this limitation. Links Report Presentation References Fu, ...
State space models (SSMs), such as Mamba, have gained prominence for their effectiveness and efficiency in modeling long-range dependencies in sequential data. However, adapting SSMs to non-sequential graph data presents a notable challenge. In this work, we introduce Graph-Mamba, the first ...
10. The results reveal that our LSKNet-T and LSKNet-S perform exceptionally well, achieving state- of-the-art mAP scores of 46.93% and 47.87% respectively, surpassing all other models by a significant margin. 4.5. Analysis Detection Results Visualization. Visualization ...