We study the problem of synthesizing programs that include machine learning components such as deep neural networks (DNNs). We focus on statistical properties, which are properties expected to hold with high probability -- e.g., that an image classification model correctly identifies people in images with high probability. We propose novel algorithms for sketching and synthesizing such programs by leveraging ideas from statistical learning theory to provide statistical soundness guarantees. We evaluate our approach on synthesizing list processing programs that include DNN components used to process image inputs, as well as case studies on image classification and on precision medicine. Our results demonstrate that our approach can be used to synthesize programs with probabilistic guarantees.
翻译:我们研究综合包括深神经网络等机器学习组件的程序的问题。我们注重统计属性,这些属性可望保持高概率 -- -- 例如图像分类模型正确识别高概率图像中的人。我们提出新的算法,通过利用统计学习理论的理念来提供统计可靠性保障,来绘制和综合这类程序。我们评估了我们关于综合列表处理程序的方法,其中包括用于处理图像输入的 DNN组件,以及关于图像分类和精密医学的案例研究。我们的结果表明,我们的方法可以用概率保障来合成程序。