September 6th, 2019
The dida machine learning scientists Lorenz Richter and Konrad Schultka participated in the conference Mathematics of Deep Learning, on the mathematical foundations of deep neural networks and their growing applications. Among others it featured talks on equivariant generalizations of convolutional neural networks (Taco Cohen), on connections to the theory of ordinary and partial differential equations (Eldad Haber), on the non-convex loss landscape of deep neural networks (René Vidal), on alternative learning strategies based on Langevin dynamics (Ben Leimkuhler) and on application to inverse problems and parametric partial differential equations (Gitta Kutyniok).
March 23rd, 2020
Mathematical models cannot cure COVID-19, but they can help us to understand how the virus spreads and to take the appropriate actions to contain the pandemic.
dida's external advisor Michael...read more