** Numerically Accurate Hyperbolic Embeddings Using Tiling-Based Models
**** Authors:
*** Introduction This repo contains official code (learning part in PyTorch) and models for the NeurIPS 2019 paper, [[http://papers.neurips.cc/paper/8476-numerically-accurate-hyperbolic-embeddings-using-tiling-based-models.pdf][Numerically Accurate Hyperbolic Embeddings Using Tiling-Based Models]]. We implemented our models under the same frame of [[https://github.com/facebookresearch/poincare-embeddings][Poincaré Embeddings for Learning Hierarchical Representations]], which is licensed under =LICENSE= and [[https://creativecommons.org/licenses/by-nc/4.0/][CC-BY-NC 4.0]]. However, we preserve the rights for commerical purpose of our tiling-based models.
** Installation Clone this repository and run following commands
git clone https://github.com/ydtydr/HyperbolicTiling_Learning.git cd HyperbolicTiling_Learning conda env create -f environment.yml source activate tiling python setup.py build_ext --inplace
** Dependencies
** Example: Embedding WordNet Mammals First generate the transitive closure of data via
cd wordnet python transitive_closure.py
This will generate the transitive closure of the full noun, verb hierarchy as well as of the mammals subtree of WordNet. We also include the Gr-QC dataset in /wordnet/ folder.
To embed the mammals subtree in the reconstruction setting, go to the /root directory/ of the project and run
./train-mammals.sh
This shell script includes the appropriate parameter settings for the mammals subtree. Similar scripts to learn embeddings of Word nouns, Verbs and Gr-QC are located at =train-nouns.sh=, =train-verbs.sh=, =train-grqc.sh=. These scripts contain the hyperparameters to reproduce the embeddings results in the paper with available models: =Poincare=, =Lorentz=, =NLorentz=, =LTiling_rsgd=, =NLTiling_rsgd=, =LTiling_sgd= and =HTiling_rsgd=. Multithreaded async SGD training is also supported.
** References If you use our code or wish to refer to our results, please use the following BibTex entry:
@incollection{yu2019numerically, title = {Numerically Accurate Hyperbolic Embeddings Using Tiling-Based Models}, author = {Yu, Tao and De Sa, Christopher M}, booktitle = {Proceedings of the 33rd Conference on Neural Information Processing Systems (NeurIPS 2019)}, month = {Oct.}, year = {2019} }