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

Abstract: Visualization and uncertainty quantification are often used to support our interpretation of deep learning models. In this talk, we show through examples how both visualization and uncertainty quantification can lead to misinterpretation if applied naïvely. Our examples will include equivariant neural networks for graphs and images, as well as uncertainty quantification with structured label variation.