Summary of Isr: Invertible Symbolic Regression, by Tony Tohme et al.
ISR: Invertible Symbolic Regression
by Tony Tohme, Mohammad Javad Khojasteh, Mohsen Sadr, Florian Meyer, Kamal Youcef-Toumi
First submitted to arxiv on: 10 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Information Theory (cs.IT); 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 This paper proposes an Invertible Symbolic Regression (ISR) method that generates analytical relationships between inputs and outputs via invertible maps. Building on the principles of Invertible Neural Networks (INNs) and Equation Learner (EQL), ISR combines the benefits of neural networks and symbolic regression. The architecture incorporates affine coupling blocks, making it end-to-end differentiable and learnable through gradient-based optimization. Additionally, sparsity-promoting regularization enables the discovery of concise and interpretable invertible expressions. Applications of ISR include density estimation tasks and solving inverse problems, such as a benchmark inverse kinematics problem and a geoacoustic inversion problem in oceanography. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ISR is a new way to find relationships between inputs and outputs using machine learning. It’s like finding the right formula to describe how something works. The method uses special types of neural networks called Invertible Neural Networks, which are different from regular neural networks because they can be easily reversed or “inverted.” This helps ISR generate simple formulas that can be used to make predictions or solve problems. The paper shows that ISR is good at solving problems like finding the best way for a robot arm to move and figuring out what’s under the ocean floor based on sound waves. |
Keywords
» Artificial intelligence » Density estimation » Machine learning » Optimization » Regression » Regularization
