Summary of Unsupervised Learning Of Hybrid Latent Dynamics: a Learn-to-identify Framework, by Yubo Ye et al.
Unsupervised Learning of Hybrid Latent Dynamics: A Learn-to-Identify Framework
by Yubo Ye, Sumeet Vadhavkar, Xiajun Jiang, Ryan Missel, Huafeng Liu, Linwei Wang
First submitted to arxiv on: 13 Mar 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 The abstract introduces the challenge of identifiability in unsupervised learning of latent dynamics from high-dimensional time-series. The paper proposes a novel framework called Meta-HyLaD, which combines physics-based inductive bias with a learn-to-identify strategy for unsupervised meta-learning of hybrid latent dynamics. This framework includes a latent dynamic function that hybridizes known mathematical expressions with neural functions to describe unknown errors. Through experiments on five physics and one biomedical systems, the paper shows that Meta-HyLaD can integrate rich prior knowledge while identifying its gap to observed data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how machines can learn about hidden patterns in data without being told what to look for. It presents a new way to do this by combining existing ideas from physics and computer science. The method is tested on different types of data, including data from physics and medicine, and shows that it works well. |
Keywords
* Artificial intelligence * Meta learning * Time series * Unsupervised
