Post #503

GML In-Depth: three forms of self-supervised learning My new in-depth newsletter on self-supervised learning with applications to graphs. There is an upcoming keynote talk from Alexei Efros at ICLR'21 about self-supervised learning and I was inspired by the motivations that he talks there. In particular, he explains that self-supervised learning is a way to reduce the role of humans in designing ML pipelines, which would allow neural nets to learn in a similar way as humans do. Self-supervised learning for graphs is an active area of research and there are good reasons for this: for applications such as drug or catalyst discovery, there are billions of unlabeled graphs from which we would like to extract as much relevant information as possible. So self-supervised learning is becoming a new paradigm for learning such useful representations.