Summary of Variational Encoder-decoders For Learning Latent Representations Of Physical Systems, by Subashree Venkatasubramanian and David A. Barajas-solano
Variational Encoder-Decoders for Learning Latent Representations of Physical Systems
by Subashree Venkatasubramanian, David A. Barajas-Solano
First submitted to arxiv on: 6 Dec 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 We present VED, a deep-learning framework that learns low-dimensional representations of physical systems’ relationships between high-dimensional parameters and responses. The VED framework consists of two probabilistic transformations: an encoder mapping parameters to latent codes and a decoder mapping codes to the response. Hyperparameters are identified by maximizing a variational lower bound on the log-conditional distribution of the response given parameters. To promote disentanglement, we add a regularization penalty that encourages the pushforward of a standard Gaussian distribution to approximate the marginal response distribution. Our framework is applicable to various physical systems and can be used for tasks such as inverse problems and system identification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Scientists are trying to understand how complex things work by using computers to learn about them. They’re creating a new way to do this called VED, which helps them figure out relationships between many numbers that describe something and the results they get from measuring it. This new method is like a recipe for making predictions and understanding systems better. It’s useful for solving puzzles and problems in science. |
Keywords
» Artificial intelligence » Decoder » Deep learning » Encoder » Regularization
