Summary of Theoretical Foundations Of Deep Selective State-space Models, by Nicola Muca Cirone et al.
Theoretical Foundations of Deep Selective State-Space Models
by Nicola Muca Cirone, Antonio Orvieto, Benjamin Walker, Cristopher Salvi, Terry Lyons
First submitted to arxiv on: 29 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Dynamical Systems (math.DS)
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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 new paper provides theoretical foundations for the recent success of structured state-space models (SSMs) in modeling sequential data. Building on the work of Gu et al., deep SSMs have demonstrated excellent performance across various domains at a lower training and inference cost compared to attention-based transformers. The study shows that by allowing multiplicative interactions between inputs and hidden states, SSMs can outperform attention-powered foundation models trained on text at scales of billion parameters. This paper provides theoretical grounding for this finding using tools from Rough Path Theory, demonstrating that the hidden state is a low-dimensional projection of the signature of the input, capturing non-linear interactions between tokens at distinct timescales. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Structured state-space models are being used to model sequential data with great success. Researchers have found that these models can be trained and inferred quickly while still performing well. A new study provides a theoretical understanding of why this is happening. The paper shows that by allowing certain types of interactions between inputs and hidden states, SSMs can outperform other types of models. |
Keywords
* Artificial intelligence * Attention * Grounding * Inference
