Code for Hyperbolic Embedding Inference for Structured Multi-Label Prediction

DOI

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.

Further information can be found in the README.md.

Identifier
DOI https://doi.org/10.18419/DARUS-3988
Related Identifier IsCitedBy https://doi.org/10.5555/3600270.3602662
Metadata Access https://darus.uni-stuttgart.de/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.18419/DARUS-3988
Provenance
Creator Xiong, Bo ORCID logo; Nayyeri, Mojtaba ORCID logo; Cochez, Michael ORCID logo; Staab, Steffen ORCID logo
Publisher DaRUS
Contributor Xiong, Bo; Staab, Steffen
Publication Year 2024
Funding Reference European Commission info:eu-repo/grantAgreement/EC/H2020/860801
Rights info:eu-repo/semantics/openAccess
OpenAccess true
Contact Xiong, Bo (Universität Stuttgart); Staab, Steffen (Universität Stuttgart)
Representation
Resource Type Code; Dataset
Format text/x-python; text/plain; application/octet-stream; image/png; text/xml-graphml; application/x-ipynb+json; text/markdown
Size 28627; 45012; 1183850; 1889541; 12075; 244235; 10224; 38585; 33293; 189206; 3747; 2908; 8683; 123; 0; 621689; 1478; 1569; 5551; 7127; 2179; 2108; 2641; 7180; 1785422; 2852700; 2246675; 3594723
Version 1.0
Discipline Computer Science; Computer Science, Electrical and System Engineering; Engineering Sciences