Summary of Mamba-360: Survey Of State Space Models As Transformer Alternative For Long Sequence Modelling: Methods, Applications, and Challenges, by Badri Narayana Patro et al.
Mamba-360: Survey of State Space Models as Transformer Alternative for Long Sequence Modelling: Methods, Applications, and Challenges
by Badri Narayana Patro, Vijay Srinivas Agneeswaran
First submitted to arxiv on: 24 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM); Image and Video Processing (eess.IV)
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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 A novel survey explores the rising star of State Space Models (SSMs) in sequence modeling, highlighting their potential to surpass traditional Recurrent Neural Networks (RNNs) and Long Short-Term Memory Networks (LSTMs). The paper categorizes foundational SSMs into three paradigms: Gating architectures, Structural architectures, and Recurrent architectures. It also showcases diverse applications across domains like vision, video, audio, speech, language, medical, chemical, recommendation systems, and time series analysis. The survey consolidates the performance of SSMs on benchmark datasets such as Long Range Arena (LRA), WikiText, Glue, Pile, ImageNet, Kinetics-400, sstv2, video datasets like Breakfast, COIN, LVU, and various time series datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary State Space Models are a new approach to sequence modeling that can handle long sequences better than traditional methods. The paper looks at many different types of SSMs and how they work. It also shows how these models can be used in lots of different areas like image recognition, video analysis, speech recognition, and more. This makes it easier for us to understand what these models are good for and how they compare to other methods. |
Keywords
» Artificial intelligence » Time series
