Summary of State-space Modeling in Long Sequence Processing: a Survey on Recurrence in the Transformer Era, by Matteo Tiezzi et al.
State-Space Modeling in Long Sequence Processing: A Survey on Recurrence in the Transformer Era
by Matteo Tiezzi, Michele Casoni, Alessandro Betti, Marco Gori, Stefano Melacci
First submitted to arxiv on: 13 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 longstanding goal in Artificial Intelligence is to develop algorithms that can learn from sequential data, retaining information about past inputs while still processing upcoming data. Researchers have proposed various solutions, simplifying the learning paradigm by limiting the processed context or dealing with sequences of limited length. The rise of Transformers has overshadowed Recurrent Neural Networks (RNNs), but RNNs are experiencing a resurgence due to advancements in State-Space models and large-context Transformers. Large Language Models have also fueled interest in efficient solutions for processing sequential data over time. This survey provides an overview of the latest approaches based on recurrent models for sequential data processing, featuring a taxonomy of architectural and algorithmic solutions. The emerging picture suggests that novel routes beyond standard Backpropagation Through Time may be explored, enabling online pattern processing leveraging local-forward computations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding better ways to learn from data that comes in a sequence, like a movie or a conversation. Researchers have been working on this problem for a long time and have proposed different solutions. One solution was to only look at a limited amount of the past data, but this has its own limitations. Another approach uses special kinds of computers called Transformers, which are very good at processing sequences. However, researchers are now looking back at older ideas called Recurrent Neural Networks (RNNs), which can also process sequences. The paper is an overview of the latest approaches to learn from sequential data and provides a guide for other researchers working in this area. |
Keywords
» Artificial intelligence » Backpropagation
