Summary of Mambamixer: Efficient Selective State Space Models with Dual Token and Channel Selection, by Ali Behrouz and Michele Santacatterina and Ramin Zabih
MambaMixer: Efficient Selective State Space Models with Dual Token and Channel Selection
by Ali Behrouz, Michele Santacatterina, Ramin Zabih
First submitted to arxiv on: 29 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 Recent breakthroughs in deep learning have largely relied on Transformers due to their data dependency and ability to learn at scale. However, the attention module in these architectures exhibits quadratic time and space complexity, limiting their scalability for long-sequence modeling. To address this issue, researchers have developed State Space Models (SSMs), including Selective State Space Models, which have shown promising results for long sequence modeling. Motivated by this success, we present MambaMixer, a novel architecture with data-dependent weights that uses a dual selection mechanism across tokens and channels, called Selective Token and Channel Mixer. Our proof-of-concept architectures, Vision MambaMixer (ViM2) and Time Series MambaMixer (TSM2), based on the MambaMixer block, achieve competitive performance in various vision and time series forecasting tasks, including ImageNet classification, object detection, semantic segmentation, and time series forecasting. Our results highlight the importance of selective mixing across both tokens and channels. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a super powerful computer that can learn from lots of data. This is called deep learning. Usually, we use something called Transformers to make this happen. But these Transformers get slow when we try to work with very long sequences of data. Some smart people came up with an idea called State Space Models (SSMs) that works really well for long sequence modeling. Inspired by this success, we created a new architecture called MambaMixer that can learn from data and make decisions based on what’s important. We tested our architecture in different areas like image recognition and predicting future events. Our results show that MambaMixer is really good at doing these tasks and does them faster than other methods. |
Keywords
* Artificial intelligence * Attention * Classification * Deep learning * Object detection * Semantic segmentation * Time series * Token
