Deep SSMs integrate the representational power of deep learning with the structured probabilistic framework of state-space models, providing a robust approach for forecasting and analyzing time-series data with underlying temporal dependencies. These models adeptly capture latent states in complex sequences, making them well-suited for applications such as finance, biomedical signal processing, and autonomous systems where understanding and predicting dynamic behaviors are crucial. By marrying deep neural networks' adaptability with the probabilistic reasoning of SSMs, deep SSMs offer enhanced flexibility and accuracy in modeling non-linear, non-Gaussian processes, leveraging techniques like variational inference and recurrent neural networks to overcome limitations inherent in traditional linear state-space methodologies.
The concept of deep SSMs emerged in the late 2010s, gaining considerable traction around 2018 as advancements in both computational power and theoretical breakthroughs in deep learning and probabilistic models enabled more sophisticated and scalable applications.
Key figures contributing to the development of deep SSMs include researchers like David Krueger and Yarin Gal, who have explored the intersection of deep learning and probabilistic modeling, as well as numerous interdisciplinary teams focusing on the practical deployment of these models in various scientific and industrial contexts.