Probabilistic reasoning for analysis of approximate computations
Saša Misailovíc · 2017
Popular application domains such as multimedia processing, machine learning, and big-data analytics operate on inherently noisy data and make decisions under uncertainty. While these applications are often good candidates for both algorithmic and system-level approximation, a major open challenge is how to analyze the influence of noisy data and candidate approximations on the application's outputs.