Summary of Paired Autoencoders For Likelihood-free Estimation in Inverse Problems, by Matthias Chung and Emma Hart and Julianne Chung and Bas Peters and Eldad Haber
Paired Autoencoders for Likelihood-free Estimation in Inverse Problems
by Matthias Chung, Emma Hart, Julianne Chung, Bas Peters, Eldad Haber
First submitted to arxiv on: 21 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Numerical Analysis (math.NA)
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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 proposed work develops a paired autoencoder framework as a likelihood-free estimator for solving nonlinear inverse problems governed by partial differential equations. The framework allows for efficient construction of solutions, overcoming limitations in generalization and accuracy when compared to traditional algorithms. This approach is demonstrated through examples from full waveform inversion and inverse electromagnetic imaging. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We develop a new way to solve tricky math problems that involve finding the original signal from some noisy measurements. Our method uses a special type of neural network called an autoencoder, which helps us get better results without needing to know the details of the problem’s underlying physics. We show how our approach can be used to solve real-world problems like imaging underground structures and analyzing seismic data. |
Keywords
» Artificial intelligence » Autoencoder » Generalization » Likelihood » Neural network
