Abstract
We start with a basic introduction to the deep learning framework Tensorflow. We then review some recent results by D. Yarotsky [1] regarding the approximation of functions in Sobolev spaces by ReLU neural networks. Finally, we will evaluate these theoretical results numerically in a hands-on session, building upon the learned Tensorflow techniques.
Joint session with Rafael Raisenhofer.
[1] Yarotsky, D. (2017). Error bounds for approximations with deep ReLU networks. Neural Networks, 94, 103-113. https://www.sciencedirect.com/science/article/abs/pii/S0893608017301545
Jan Macdonald
My research is at the interface of applied and computational mathematics and scientific machine learning. I am interested in inverse problems, signal- and image recovery, and robust and interpretable deep learning.