Elucidating the Hierarchical Nature of Behavior with Masked Autoencoders
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######### Elucidating the Hierarchical Nature of Behavior with Masked Autoencoders ######### Authors: Lucas Stoffl, Andy Bonnetto, Stéphane D'Ascoli & Alexander Mathis Affiliation: Ecole Polytechnique de Lausanne (EPFL) Date: 25/09/2024 Link to the BiorXiv article : https://doi.org/10.1101/2024.08.06.606796 ----------------- Provided data (hBehaveMAE checkpoints) We provide a collection of pre-trained models that were reported in our paper, allowing you to reproduce our results for MABe22, hBABEL and Shot7M2 datasets. Note that you can download Shot7M2 on HuggingFace and generate hBABEL by following the instructions on the github page. hBehaveMAE_hBABEL.pth : checkpoint for the hBehaveMAE pre-trained on the hBABEL dataset hBehaveMAE_Shot7M2.pth : checkpoint for the hBehaveMAE pre-trained on the Shot7M2 dataset hBehaveMAE_MABe22.pth: checkpoint for the hBehaveMAE pre-trained on the MABe22 dataset References If you find our code, weights or ideas useful, please cite: @article {Stoffl2024hBehaveMAE, author = {Stoffl, Lucas and Bonnetto, Andy and d{\textquoteright}Ascoli, St{\'e}phane and Mathis, Alexander}, title = {Elucidating the Hierarchical Nature of Behavior with Masked Autoencoders}, elocation-id = {2024.08.06.606796}, year = {2024}, doi = {10.1101/2024.08.06.606796}, publisher = {Cold Spring Harbor Laboratory}, URL = {https://www.biorxiv.org/content/early/2024/08/08/2024.08.06.606796}, eprint = {https://www.biorxiv.org/content/early/2024/08/08/2024.08.06.606796.full.pdf}, journal = {bioRxiv}}
