Summary of State-space Models Can Learn In-context by Gradient Descent, By Neeraj Mohan Sushma et al.
State-space models can learn in-context by gradient descent
by Neeraj Mohan Sushma, Yudou Tian, Harshvardhan Mestha, Nicolo Colombo, David Kappel, Anand Subramoney
First submitted to arxiv on: 15 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
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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 state-space models (Deep SSMs) are a type of sequence data model that has shown promise in learning sequence data. They have been compared to transformers, which also perform in-context learning. However, there was a gap in understanding how Deep SSMs could learn in this way. This study provides a direct and explicit construction to demonstrate that state-space models can perform gradient-based learning for in-context learning, similar to transformers. The authors propose a single structured state-space model layer with multiplicative input and output gating, which can reproduce the outputs of an implicit linear model after one step of gradient descent. They then extend this to multi-step linear and non-linear regression tasks. The authors validate their construction by training randomly initialized augmented SSMs on these tasks. The empirically obtained parameters match those predicted analytically through the theoretical construction. This study provides insights into the role of input- and output-gating in recurrent architectures, enabling expressive power typical of foundation models. It also explores the relationship between state-space models and linear self-attention, revealing their ability to learn in-context. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to understand how computers can learn from data by looking at past events. It’s like trying to remember what you did yesterday or last week. The authors show that this type of learning, called “in-context learning”, can be done using special models called state-space models. They provide a simple and clear explanation for how these models work, including how they can learn from data and make predictions about the future. The authors also test their ideas by training computers on different tasks and showing that they work well. This study helps us understand how computers can use past experiences to make better decisions in the present. |
Keywords
» Artificial intelligence » Gradient descent » Linear regression » Self attention
