Summary of Variational Neural Stochastic Differential Equations with Change Points, by Yousef El-laham et al.
Variational Neural Stochastic Differential Equations with Change Points
by Yousef El-Laham, Zhongchang Sun, Haibei Zhu, Tucker Balch, Svitlana Vyetrenko
First submitted to arxiv on: 1 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 neural stochastic differential equation (neural SDE) is a type of mathematical model used for modeling change points in time-series data. This paper proposes a novel approach to training neural SDEs using the variational autoencoder (VAE) framework, which only requires a Gaussian prior on the initial state rather than the entire latent process. The authors develop two methodologies for detecting change points: maximum likelihood-based and sequential likelihood ratio test-based approaches. They also provide theoretical analysis of the proposed scheme and demonstrate its effectiveness in modeling both classical SDEs and real-world datasets with distribution shifts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to model changes in time-series data using special equations called neural stochastic differential equations (SDEs). Researchers have developed a method that uses another technique called variational autoencoders (VAEs) to train these SDEs. The approach only needs information about the starting point of the process, not the whole thing. They also came up with two ways to find changes in the data: one based on probability and another using ratios. This paper shows how well their method works by testing it on simple equations and real-life datasets. |
Keywords
» Artificial intelligence » Likelihood » Probability » Time series » Variational autoencoder
