Summary of Function Extrapolation with Neural Networks and Its Application For Manifolds, by Guy Hay et al.
Function Extrapolation with Neural Networks and Its Application for Manifolds
by Guy Hay, Nir Sharon
First submitted to arxiv on: 17 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 paper proposes a novel approach for estimating a function on one domain when only discrete samples are available on another domain. A neural network is trained to incorporate prior knowledge of the function, and an error bound is obtained over the extrapolation domain. The condition number is defined to quantify the level of difficulty of the setup. Compared to transformers-based time series prediction methods, this approach is suitable for general subdomains and manifolds. An improved loss function is constructed to boost accuracy and robustness. Numerical tests and comparisons are conducted, demonstrating the effectiveness of the approach in various scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a tricky problem where we only have samples from one place, but we want to estimate what’s happening elsewhere. The solution uses a special kind of computer program called a neural network that helps us figure out what the function should be like based on what we already know. By looking at the problem carefully, we can even estimate how much our answer might be off, which is helpful when trying to predict things. This approach works better than some other methods in certain situations, and it’s useful for understanding complex shapes and patterns. |
Keywords
» Artificial intelligence » Loss function » Neural network » Time series
