Summary of On the Resurgence Of Recurrent Models For Long Sequences — Survey and Research Opportunities in the Transformer Era, by Matteo Tiezzi et al.
On the Resurgence of Recurrent Models for Long Sequences – Survey and Research Opportunities in the Transformer Era
by Matteo Tiezzi, Michele Casoni, Alessandro Betti, Tommaso Guidi, Marco Gori, Stefano Melacci
First submitted to arxiv on: 12 Feb 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 This paper explores the development of machine learning models that can process and learn from very long sequences of data. The current state-of-the-art Transformer-based networks have led to a focus on parallel attention mechanisms, obscuring the role of classic sequential processing techniques used in Recurrent Models. However, recent advancements have brought forth novel neural architectures that combine the strengths of both Transformers and Recurrent Nets. Additionally, Deep Space-State Models have emerged as robust approaches for function approximation over time, opening new avenues for learning from sequential data. This survey aims to provide an overview of these trends under the unifying umbrella of Recurrence, highlighting novel research opportunities that arise when considering potentially infinite-length sequences. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how machine learning models can handle very long streams of data. Right now, the best models are called Transformers, which use parallel attention to process information. This has led some researchers to focus on just using attention and forget about traditional sequential processing techniques like Recurrent Models. But other researchers have been working on new neural networks that combine the strengths of both Transformers and Recurrent Nets. They’ve also developed a type of model called Deep Space-State Models, which are really good at learning from long sequences over time. This survey will help us understand these trends better and think about new ways to improve our models. |
Keywords
* Artificial intelligence * Attention * Machine learning * Transformer
