Summary of Evaluating K-fold Cross Validation For Transformer Based Symbolic Regression Models, by Kaustubh Kislay et al.
Evaluating K-Fold Cross Validation for Transformer Based Symbolic Regression Models
by Kaustubh Kislay, Shlok Singh, Soham Joshi, Rohan Dutta, Jay Shim George Flint, Kevin Zhu
First submitted to arxiv on: 29 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 paper investigates the application of k-fold cross-validation to a transformer-based symbolic regression model, aiming to improve its performance on smaller datasets. By partitioning the training data into multiple subsets and iteratively training and validating on each subset, the technique aims to provide an estimate of model generalization and mitigate overfitting issues. The results show that this process improves the model’s output consistency and generalization by a significant relative improvement in validation loss. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores ways to make symbolic regression more efficient and accessible using transformer models. It finds a way to improve the performance of these models on smaller datasets, which can be useful in resource-constrained environments. By applying k-fold cross-validation, the model becomes better at generalizing to new data and produces more consistent results. |
Keywords
» Artificial intelligence » Generalization » Overfitting » Regression » Transformer
