Seminar Geometry, Algebra and Physics in Deep Neural Networks (GAPinDNNs)

Abstract: Numerical linear algebra underpins all computational sciences, machine learning not the least. But what if machine learning could return the favor by learning numerical algorithms tailored to a particular problem class? In this talk, I will highlight the connection between matrices and graph, and argue that this makes graph neural networks a natural fit for learning task-specific numerical algorithms.