用Transformer预测积分方法是否有效，准确率提升30%。
Transformers to Predict the Applicability of Symbolic Integration Routines
- 用Transformer学习判断积分方法能否成功
- 准确率提升30%，精度达70%优于人工规则
- 推理快可嵌入计算机代数系统，适合专家解释
符号积分是数学中的基础问题。本文研究如何利用机器学习优化计算机代数系统（CAS）中的这一任务。我们训练Transformer模型来预测特定积分方法是否成功，并与主流CAS中的人工启发式规则（称为守卫）进行比较。结果表明，Transformer在准确率上最高提升30%，精度达到70%，显著优于现有规则。此外，Transformer的推理时间可忽略不计，表明其适合集成到CAS中作为守卫。我们还使用层内梯度（Layer Integrated Gradients）分析模型决策，结合领域专家指导，可基于输入标记解释部分预测逻辑，为后续优化提供线索。
原文摘要 · Abstract (English)
Symbolic integration is a fundamental problem in mathematics: we consider how machine learning may be used to optimise this task in a Computer Algebra System (CAS). We train transformers that predict whether a particular integration method will be successful, and compare against the existing human-made heuristics (called guards) that perform this task in a leading CAS. We find the transformer can outperform these guards, gaining up to 30% accuracy and 70% precision. We further show that the inference time of the transformer is inconsequential which shows that it is well-suited to include as a guard in a CAS. Furthermore, we use Layer Integrated Gradients to interpret the decisions that the transformer is making. If guided by a subject-matter expert, the technique can explain some of the predictions based on the input tokens, which can lead to further optimisations.
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