Summary of Simba: Simplified Mamba-based Architecture For Vision and Multivariate Time Series, by Badri N. Patro and Vijay S. Agneeswaran
SiMBA: Simplified Mamba-Based Architecture for Vision and Multivariate Time series
by Badri N. Patro, Vijay S. Agneeswaran
First submitted to arxiv on: 22 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Image and Video Processing (eess.IV); Systems and Control (eess.SY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes SiMBA, a new architecture that addresses issues with attention networks in transformers. By introducing Einstein FFT (EinFFT) for channel modeling and using the Mamba block for sequence modeling, SiMBA outperforms existing State Space Models (SSMs) on image and time-series benchmarks. Specifically, 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 and seven time series benchmark datasets. The proposed architecture is designed to handle longer sequence lengths and improve performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SiMBA is a new type of State Space Model that uses Einstein FFT for channel modeling and the Mamba block for sequence modeling. This helps to improve the performance of SSMs on image and time-series benchmarks. SiMBA is particularly good at handling long sequences of data and can be used in tasks such as computer vision and natural language processing. |
Keywords
* Artificial intelligence * Attention * Natural language processing * Time series * Transfer learning
