Machine learning (ML) is increasingly seen as a viable approach for building compiler optimization heuristics, but many ML methods cannot replicate even the simplest of the data flow analyses that are critical to making good optimization decisions. We posit that if ML cannot do that, then it is insufficiently able to reason about programs. We formulate data flow analyses as supervised learning tasks and introduce a large open dataset of programs and their corresponding labels from several analyses. We use this dataset to benchmark ML methods and show that they struggle on these fundamental program reasoning tasks. We propose ProGraML - Program Graphs for Machine Learning - a language-independent, portable representation of program semantics. ProGraML overcomes the limitations of prior works and yields improved performance on downstream optimization tasks.

Due to its computational simplicity, the PSNR is a popular and widely used image quality measure, although it correlates poorly with perceived visual quality. Distortion sensitivity, a reference image specific property, can be used to compensate for the lack of perceptual relevance of the PSNR. Based on the functional mapping between perceptual and computational quality a deep convolutional neural network is used to estimate patchwise distortion sensitivity. The local estimates are used for an imagewise perceptual adaptation of the PSNR. The performance of the proposed estimation approach is evaluated on the LIVE and TID2013 databases and shows comparable or superior performance as compared to benchmark image quality measures.