Summary of Embed and Emulate: Contrastive Representations For Simulation-based Inference, by Ruoxi Jiang et al.
Embed and Emulate: Contrastive representations for simulation-based inference
by Ruoxi Jiang, Peter Y. Lu, Rebecca Willett
First submitted to arxiv on: 27 Sep 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 new SBI method, Embed and Emulate (E&E), efficiently handles high-dimensional data and complex, multimodal parameter posteriors by learning a low-dimensional latent embedding of the data and a corresponding fast emulator in the latent space. This eliminates the need to run expensive simulations or a high-dimensional emulator during inference. E&E outperforms existing methods in a realistic, non-identifiable parameter estimation task using the high-dimensional, chaotic Lorenz 96 system. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Scientists are trying to improve how they fit models and calibrate computer simulations with real-world data. One way is by using something called simulation-based inference (SBI). This involves making a fake dataset that looks like the real one, then using it to figure out what’s going on in the real world. Some SBI methods use machine learning tools to make this process faster and more accurate. However, these approaches can be tricky when dealing with very big datasets or complex systems. A new method called Embed and Emulate (E&E) is trying to solve this problem by creating a shortcut to find important information in the data. |
Keywords
» Artificial intelligence » Embedding » Inference » Latent space » Machine learning
