SiMBA：用于视觉和多元时间序列的简化Mamba架构
SiMBA: Simplified Mamba-Based Architecture for Vision and Multivariate Time series
AI总结：
针对Mamba在视觉大型网络中的稳定性问题，提出SiMBA架构，通过EinFFT进行通道建模和Mamba块进行序列建模，在图像和时间序列基准上优于现有SSM并弥合与Transformers的性能差距。
摘要：
Transformers广泛采用注意力网络进行序列混合，并使用MLP进行通道混合，在跨领域实现突破方面发挥了关键作用。然而，近期文献强调了注意力网络的问题，包括归纳偏置低以及关于输入序列长度的二次复杂度。S4等状态空间模型（SSMs）及其他模型（Hippo、Global Convolutions、liquid S4、LRU、Mega和Mamba）相继出现以解决上述问题，帮助处理更长的序列长度。Mamba作为最先进的SSM，在针对计算机视觉数据集扩展到大型网络时存在稳定性问题。我们提出了SiMBA，一种新架构，通过特定的特征值计算引入Einstein FFT（EinFFT）进行通道建模，并使用Mamba块进行序列建模。跨图像和时间序列基准的广泛性能研究表明，SiMBA优于现有的SSMs，弥合了与最先进Transformers的性能差距。值得注意的是，SiMBA在ImageNet和迁移学习基准（如Stanford Car和Flower）以及任务学习基准，还有七个时间序列基准数据集上，确立了其作为新最先进SSM的地位。项目页面可在该网站获取：~\url{https://github.com/badripatro/Simba}。
英文摘要：
Transformers have widely adopted attention networks for sequence mixing and MLPs for channel mixing, playing a pivotal role in achieving breakthroughs across domains. However, recent literature highlights issues with attention networks, including low inductive bias and quadratic complexity concerning input sequence length. State Space Models (SSMs) like S4 and others (Hippo, Global Convolutions, liquid S4, LRU, Mega, and Mamba), have emerged to address the above issues to help handle longer sequence lengths. Mamba, while being the state-of-the-art SSM, has a stability issue when scaled to large networks for computer vision datasets. We propose SiMBA, a new architecture that introduces Einstein FFT (EinFFT) for channel modeling by specific eigenvalue computations and uses the Mamba block for sequence modeling. Extensive performance studies across image and time-series benchmarks demonstrate that SiMBA outperforms existing SSMs, bridging the performance gap with state-of-the-art transformers. Notably, SiMBA establishes itself as the new state-of-the-art SSM on ImageNet and transfer learning benchmarks such as Stanford Car and Flower as well as task learning benchmarks as well as seven time series benchmark datasets. The project page is available on this website ~\url{https://github.com/badripatro/Simba}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
