报告题目: IDE Support for Machine Learning Programming
报告人： Julian Dolby 研究员 美国IBM Thomas J. Watson Research Center
主持人： 李鑫 副研究员
报告时间: 10月17日 周三10:00—11:00
报告地址: 中北校区数学馆201
报告摘要：
Machine learning has transformed domains like vision and translation, and is now increasingly used in science, where the correctness of such code is vital. Python is popular for machine learning, in part because of its wealth of machine learning libraries, and is felt to make development faster; however, this dynamic language has less support for error detection at code creation time than tools like Eclipse. This is especially problematic for machine learning: given its statistical nature, code with subtle errors may run and produce results that look plausible but are meaningless. This can vitiate scientific results.
We report on Ariadne: an infrastructure for applying a static analysis framework, WALA, to machine learning code that uses TensorFlow. We describe how we make analysis useful to developers by surfacing it in a collection of tools such as hovers and quick fixes across multiple IDE's for Python. We focus on helping developers understand tensor shapes in their code, and find and fix issues. We illustrate how Ariadne provides information in IDE's and discuss its implementation.
报告人简介：
Julian Dolby is a Research Staff Member at the IBM Thomas J. Watson Research Center, where he works on program analysis for a range of programming languages. He is one of the original creators of WALA; his recent WALA work has focused on creating the WALA Mobile infrastructure. He has co-presented tutorials on WALA at several PLDI conferences, and co-organized the Workshop on WALA at PLDI 2015.
