用Transformer实现符号计算，让非专家也能轻松上手。
CALT: A Library for Computer Algebra with Transformer
- 基于Transformer的端到端学习框架，处理符号表达式转换
- 无需专业知识即可训练模型完成代数化简等任务
- 适合想尝试符号计算的机器学习初学者或研究者
近年来的人工智能进展表明，通过端到端深度学习可实现符号计算的可学习性。在足够多的符号表达式变换示例基础上，具备强大序列到序列建模能力的Transformer模型可被训练以模拟计算过程。这一发展催生了若干引人深思的挑战与新研究方向，亟需符号计算领域积极参与。本文提出面向Transformer的符号计算库CALT，一个用户友好的Python工具库，旨在帮助深度学习非专业人士训练用于符号计算任务的模型。
原文摘要 · Abstract (English)
Recent advances in artificial intelligence have demonstrated the learnability of symbolic computation through end-to-end deep learning. Given a sufficient number of examples of symbolic expressions before and after the target computation, Transformer models - highly effective learners of sequence-to-sequence functions - can be trained to emulate the computation. This development opens up several intriguing challenges and new research directions, which require active contributions from the symbolic computation community. In this work, we introduce Computer Algebra with Transformer (CALT), a user-friendly Python library designed to help non-experts in deep learning train models for symbolic computation tasks.
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