4 papers
Accuracy estimation of neural networks by extreme value theory
Gero Junike, Marco Oesting
Neural networks are able to approximate any continuous function on a compact set. However, it is not obvious how to quantify the error of the neural network, i.e., the remaining bi…
Batch normalization does not improve initialization
Joris Dannemann, Gero Junike
Batch normalization is one of the most important regularization techniques for neural networks, significantly improving training by centering the layers of the neural network. Ther…
Precise quantile function estimation from the characteristic function
Gero Junike
We provide theoretical error bounds for the accurate numerical computation of the quantile function given the characteristic function of a continuous random variable. We show theor…
Enhancing Fourier pricing with machine learning
Gero Junike, Hauke Stier
Fourier pricing methods such as the Carr-Madan formula or the COS method are classic tools for pricing European options for advanced models such as the Heston model. These methods…