%0 Generic %1 https://doi.org/10.18419/darus-3988 %A Xiong, Bo %A Nayyeri, Mojtaba %A Cochez, Michael %A Staab, Steffen %D 2024 %I DaRUS %K dataset %R 10.18419/DARUS-3988 %T Code for Hyperbolic Embedding Inference for Structured Multi-Label Prediction %U https://darus.uni-stuttgart.de/citation?persistentId=doi:10.18419/darus-3988 %X This is a PyTorch implementation of the paper Hyperbolic Embedding Inference for Structured Multi-Label Prediction published in NeurIPS 2022. The code provides the Python scripts to reproduce the experiments in the paper, as well as a proof-of-concept example of the method. To execute the code, follow the instructions in the README.md file. For more info, please check the paper. Please have no hesitation to contact the authors for any inquiries.

@dataset{https://doi.org/10.18419/darus-3988, abstract = {This is a PyTorch implementation of the paper Hyperbolic Embedding Inference for Structured Multi-Label Prediction published in NeurIPS 2022. The code provides the Python scripts to reproduce the experiments in the paper, as well as a proof-of-concept example of the method. To execute the code, follow the instructions in the README.md file. For more info, please check the paper. Please have no hesitation to contact the authors for any inquiries.}, added-at = {2024-11-10T12:02:22.000+0100}, author = {Xiong, Bo and Nayyeri, Mojtaba and Cochez, Michael and Staab, Steffen}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2eedf02ccc7a74d44cda59ef4b2e1dd6c/analyticcomp}, doi = {10.18419/DARUS-3988}, interhash = {9257c314a4398c6436b56d2695c56a2f}, intrahash = {eedf02ccc7a74d44cda59ef4b2e1dd6c}, keywords = {dataset}, publisher = {DaRUS}, timestamp = {2024-11-10T12:02:22.000+0100}, title = {Code for Hyperbolic Embedding Inference for Structured Multi-Label Prediction}, url = {https://darus.uni-stuttgart.de/citation?persistentId=doi:10.18419/darus-3988}, year = 2024 }