Abstract
If you want to build a high-quality machine learning product, build a large, high-quality training set. At first glance, this seems as useful as the statement “if you want to be rich, get a lot of money.” However, a key idea driving our work is that new theoretical and systems concepts including weak supervision, automatic data augmentation policies, and more, can enable engineers to build training sets more quickly and cost effectively.
Along with state-of-the-art results on benchmarks, these concepts have allowed our group and collaborators to build a range of state-of-the-art applications including patient-care monitoring on electronic health records, automatic triage systems for radiologists, and enabling cardiologists to spot rare abnormalities in video MRI—along with widely used products from Apple and Google. This talk describes the theoretical and systems challenges that such applications create.
