Summary of Deep Learning-based Approaches For State Space Models: a Selective Review, by Jiahe Lin et al.
Deep Learning-based Approaches for State Space Models: A Selective Review
by Jiahe Lin, George Michailidis
First submitted to arxiv on: 15 Dec 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Other Statistics (stat.OT)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 network-based approaches for state-space models (SSMs) have gained significant attention recently, offering a powerful framework for analyzing dynamical systems. This paper provides a comprehensive review of recent advancements in discrete-time and continuous-time deep SSMs, including latent neural Ordinary Differential and Stochastic Differential Equations. The authors also discuss the classical maximum likelihood-based approach and variational autoencoder-based pipelines for learning SSMs. Representative deep learning models that fall under the SSM framework are examined, along with very recent developments where SSMs are used as standalone architectural modules to improve efficiency in sequence modeling. Examples involving mixed-frequency and irregularly-spaced time series data demonstrate the advantage of SSMs in these settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how deep neural networks can be used for state-space models (SSMs). SSMs help us understand complex systems by looking at what’s happening inside them over time. The authors show how recent advances in computer science have made it possible to use deep learning for SSMs, which is important because it lets us analyze systems better and make more accurate predictions. |
Keywords
* Artificial intelligence * Attention * Deep learning * Likelihood * Neural network * Time series * Variational autoencoder
