摘要
Recent advances in machine learning are currently influencing the way we gather data,recognize patterns,and build predictive models across a wide range of scientific disciplines.Noticeable successes include solutions in image and voice recognition that have already become part of our everyday lives,mainly enabled by algorithmic developments,hardware advances,and,of course,the availability of massive data-sets.Many of such predictive tasks are currently being tackled using over-parameterized,black-box discriminative models such as deep neural networks,in which theoretical rigor,interpretability and adherence to first physical principles are often sacrificed in favor of flexibility in representation and scalability in computation.
