%0 Journal Article %1 oladyshkin2023arbitrary %A Oladyshkin, Sergey %A Praditia, Timothy %A Kroeker, Ilja %A Mohammadi, Farid %A Nowak, Wolfgang %A Otte, Sebastian %D 2023 (accepted) %I Elsevier %J Neural Networks %K %T The Deep Arbitrary Polynomial Chaos Neural Network or how Deep Artificial Neural Networks could benefit from Data-Driven Homogeneous Chaos Theory %U https://arxiv.org/abs/2306.14753

@article{oladyshkin2023arbitrary, added-at = {2023-06-28T09:36:07.000+0200}, author = {Oladyshkin, Sergey and Praditia, Timothy and Kroeker, Ilja and Mohammadi, Farid and Nowak, Wolfgang and Otte, Sebastian}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/208e089909539ea0f0cd188044728f809/sscheurer}, interhash = {f9b9764fe5ec6639baeae0315f267b88}, intrahash = {08e089909539ea0f0cd188044728f809}, journal = {Neural Networks}, keywords = {}, publisher = {Elsevier}, timestamp = {2023-06-28T09:36:55.000+0200}, title = {The Deep Arbitrary Polynomial Chaos Neural Network or how Deep Artificial Neural Networks could benefit from Data-Driven Homogeneous Chaos Theory}, url = {https://arxiv.org/abs/2306.14753}, year = {2023 (accepted)} }