Summary of State Space Models Are Provably Comparable to Transformers in Dynamic Token Selection, by Naoki Nishikawa and Taiji Suzuki
State Space Models are Provably Comparable to Transformers in Dynamic Token Selection
by Naoki Nishikawa, Taiji Suzuki
First submitted to arxiv on: 29 May 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 Deep neural networks based on state space models (SSMs) have garnered significant attention in sequence modeling due to their computational efficiency compared to Transformers. While experiments have demonstrated the capabilities of SSMs across various tasks, theoretical understanding remains limited. This paper explores the combination of SSMs with fully connected neural networks and shows that they are comparable to Transformers in extracting essential tokens based on input. The authors consider two synthetic tasks that challenge a single SSM layer, demonstrating the efficiency of SSMs combined with nonlinear layers in solving these tasks. Additionally, the study proves the equivalence of SSMs and Transformers in estimating functions belonging to a certain class through nonparametric regression analysis. Keywords: state space models, sequence modeling, Transformers, fully connected neural networks, synthetic tasks, nonparametric regression. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using special kinds of artificial intelligence (AI) called state space models (SSMs). SSMs are good at processing sequences of information, like words or sounds. The researchers wanted to see if combining SSMs with another type of AI, called fully connected neural networks, would make them even better. They found that this combination is just as good as a more complex kind of AI called Transformers at doing certain tasks. They tested it on some fake problems and showed that it can solve them quickly and accurately. This study helps us understand how SSMs work and how we can use them to do cool things with AI. |
Keywords
» Artificial intelligence » Attention » Regression
