论文标题
部分可观测时空混沌系统的无模型预测
WhyGen: Explaining ML-powered Code Generation by Referring to Training Examples
论文作者
论文摘要
储层计算是预测湍流的有力工具，其简单的架构具有处理大型系统的计算效率。然而，其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间，然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用，该系统模拟了湍流的若干特征。我们表明，使用径向基函数作为非线性投影器，即使只有部分观测并且不知道控制方程，也能稳健地捕捉复杂的系统非线性。最后，我们表明，当测量稀疏、不完整且带有噪声，甚至控制方程变得不准确时，我们的网络仍然可以产生相当准确的预测，从而为实际湍流系统的无模型预测铺平了道路。
Deep learning has demonstrated great abilities in various code generation tasks. However, despite the great convenience for some developers, many are concerned that the code generators may recite or closely mimic copyrighted training data without user awareness, leading to legal and ethical concerns. To ease this problem, we introduce a tool, named WhyGen, to explain the generated code by referring to training examples. Specifically, we first introduce a data structure, named inference fingerprint, to represent the decision process of the model when generating a prediction. The fingerprints of all training examples are collected offline and saved to a database. When the model is used at runtime for code generation, the most relevant training examples can be retrieved by querying the fingerprint database. Our experiments have shown that WhyGen is able to precisely notify the users about possible recitations and highly similar imitations with a top-10 accuracy of 81.21%. The demo video can be found at https://youtu.be/EtoQP6850To.
