تقرير
Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients
| العنوان: | Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients |
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| المؤلفون: | Petersen, Brenden K., Landajuela, Mikel, Mundhenk, T. Nathan, Santiago, Claudio P., Kim, Soo K., Kim, Joanne T. |
| المصدر: | International Conference on Learning Representations, 2021 |
| سنة النشر: | 2019 |
| المجموعة: | Computer Science Statistics |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Statistics - Machine Learning |
| الوصف: | Discovering the underlying mathematical expressions describing a dataset is a core challenge for artificial intelligence. This is the problem of $\textit{symbolic regression}$. Despite recent advances in training neural networks to solve complex tasks, deep learning approaches to symbolic regression are underexplored. We propose a framework that leverages deep learning for symbolic regression via a simple idea: use a large model to search the space of small models. Specifically, we use a recurrent neural network to emit a distribution over tractable mathematical expressions and employ a novel risk-seeking policy gradient to train the network to generate better-fitting expressions. Our algorithm outperforms several baseline methods (including Eureqa, the gold standard for symbolic regression) in its ability to exactly recover symbolic expressions on a series of benchmark problems, both with and without added noise. More broadly, our contributions include a framework that can be applied to optimize hierarchical, variable-length objects under a black-box performance metric, with the ability to incorporate constraints in situ, and a risk-seeking policy gradient formulation that optimizes for best-case performance instead of expected performance. Comment: Published at International Conference on Learning Representations, 2021 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/1912.04871 |
| رقم الأكسشن: | edsarx.1912.04871 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |