%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 %I Elsevier %J Neural Networks %K EXC2075 MissingProject PN5 selected %P 85-104 %R 10.1016/j.neunet.2023.06.036 %T The Deep Arbitrary Polynomial Chaos Neural Network or how Deep Artificial Neural Networks could benefit from Data-Driven Homogeneous Chaos Theory %V 166

@article{oladyshkin2023arbitrary, added-at = {2024-03-26T11:56:40.000+0100}, 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/2b586ce195d36c3c24f062cd51030ba53/exc2075}, doi = {10.1016/j.neunet.2023.06.036}, interhash = {f9b9764fe5ec6639baeae0315f267b88}, intrahash = {b586ce195d36c3c24f062cd51030ba53}, journal = {Neural Networks}, keywords = {EXC2075 MissingProject PN5 selected}, month = {09}, pages = {85-104}, publisher = {Elsevier}, timestamp = {2024-03-27T16:24:54.000+0100}, title = {The Deep Arbitrary Polynomial Chaos Neural Network or how Deep Artificial Neural Networks could benefit from Data-Driven Homogeneous Chaos Theory}, volume = 166, year = 2023 }