Tue 25 Jun 2019 14:20 - 14:40 at 224AB - Reasoning and Optimizing ML Models Chair(s): Martin Maas

Most traditional software systems are not built with the artificial intelligence support (AI) in mind. Among them, some may require human interventions to operate, e.g., the manual specification of the parameters in the data processing programs, or otherwise, would behave poorly. We propose a novel framework called Autonomizer to autonomize these systems by installing the AI into the traditional programs. Autonomizeris general so it can be applied to many real-world applications. We provide the primitives and the run-time support, where the primitives abstract common tasks of autonomization and the runtime support realizes them transparently. With the support of Autonomizer, the users can gain the AI support with little engineering efforts. Like many other AI applications, the challenge lies in the feature selection, which we address by proposing multiple automated strategies based on the program analysis. Our experiment results on nine real-world applications show that the autonomization only requires adding a few lines to the source code.Besides, for the data-processing programs, Autonomizer improves the output quality by 161% on average over the default settings. For the interactive programs such as game/driving,Autonomizer achieves higher success rate with lower training time than existing autonomized programs.

Tue 25 Jun
Times are displayed in time zone: Tijuana, Baja California change

14:00 - 15:30: Reasoning and Optimizing ML ModelsPLDI Research Papers at 224AB
Chair(s): Martin MaasGoogle
14:00 - 14:20
Talk
An Inductive Synthesis Framework for Verifiable Reinforcement Learning
PLDI Research Papers
He ZhuRutgers University, USA, Zikang XiongPurdue University, Stephen Magill, Suresh JagannathanPurdue University
Media Attached
14:20 - 14:40
Talk
Programming Support for Autonomizing Software
PLDI Research Papers
Wen-Chuan LeePurdue University, Peng LiuPurdue University, Yingqi LiuPurdue University, USA, Shiqing MaPurdue University, USA, Xiangyu ZhangPurdue University
14:40 - 15:00
Talk
Wootz: A Compiler-Based Framework for Fast CNN Pruning via Composability
PLDI Research Papers
Hui GuanNorth Carolina State University, Xipeng ShenNorth Carolina State University, Seung-Hwan LimOak Ridge National Laboratory, USA
Media Attached File Attached
15:00 - 15:20
Talk
Optimization and Abstraction: A Synergistic Approach for Analyzing Neural Network Robustness
PLDI Research Papers
Greg AndersonUniversity of Texas at Austin, USA, Shankara PailoorUniversity of Texas at Austin, USA, Isil DilligUT Austin, Swarat ChaudhuriRice University
Media Attached