%0 Journal Article %1 guibas2021adaptive %A Guibas, John %A Mardani, Morteza %A Li, Zongyi %A Tao, Andrew %A Anandkumar, Anima %A Catanzaro, Bryan %D 2021 %J arXiv preprint arXiv:2111.13587 %K ecoapplication ecomodelling %T Adaptive fourier neural operators: Efficient token mixers for transformers %U https://arxiv.org/pdf/2111.13587.pdf

@article{guibas2021adaptive, added-at = {2023-12-04T13:24:38.000+0100}, author = {Guibas, John and Mardani, Morteza and Li, Zongyi and Tao, Andrew and Anandkumar, Anima and Catanzaro, Bryan}, biburl = {https://www.bibsonomy.org/bibtex/2bd4b8dd25c7ffa24b2b64f91eb668633/jascal_panetzky}, interhash = {5b95669257f8f06fda7a9b93d0fcce05}, intrahash = {bd4b8dd25c7ffa24b2b64f91eb668633}, journal = {arXiv preprint arXiv:2111.13587}, keywords = {ecoapplication ecomodelling}, timestamp = {2023-12-04T13:24:38.000+0100}, title = {Adaptive fourier neural operators: Efficient token mixers for transformers}, url = {https://arxiv.org/pdf/2111.13587.pdf}, year = 2021 }