%Aigaion2 BibTeX export from Idiap Publications
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@INPROCEEDINGS{Mai_ACL2023_2023,
         author = {Mai, Florian and Pannatier, Arnaud and Fehr, Fabio and Chen, Haolin and Marelli, Fran{\c c}ois and Fleuret, Francois and Henderson, James},
       projects = {Idiap, LAOS, NAST, COMPBIO},
          month = jul,
          title = {HyperMixer: An MLP-based Low Cost Alternative to Transformers},
      booktitle = {Proc. of the 61st Annual Meeting of the Association for Computational Linguistics},
           year = {2023},
          pages = {15632-15654},
       location = {Toronto, Canada},
   organization = {Association for Computational Linguistics},
           isbn = {978-1-959429-72-2},
            doi = {http://dx.doi.org/10.18653/v1/2023.acl-long.871},
       abstract = {Transformer-based architectures are the model of choice for natural language understanding, but they come at a significant cost, as they have quadratic complexity in the input length, require a lot of training data, and can be difficult to tune. In the pursuit of lower costs, we investigate simple MLP-based architectures. We find that existing architectures such as MLPMixer, which achieves token mixing through a static MLP applied to each feature independently, are too detached from the inductive biases required for natural language understanding. In this paper, we propose a simple variant, HyperMixer, which forms the token mixing MLP dynamically using hypernetworks. Empirically, we demonstrate that our model performs better than alternative MLP-based models, and on par with Transformers. In contrast to Transformers, HyperMixer achieves these results at substantially lower costs in terms of processing time, training data, and hyperparameter tuning.},
            pdf = {https://publications.idiap.ch/attachments/papers/2023/Mai_ACL2023_2023.pdf}
}