@@ -68,4 +68,4 @@ Training and evaluation a model using our library is done in 03 lines of code on
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@@ -68,4 +68,4 @@ Training and evaluation a model using our library is done in 03 lines of code on
[4]:[Mathieu, Emile, et al. "Continuous hierarchical representations with poincaré variational auto-encoders." Advances in neural information processing systems 32 (2019).](https://proceedings.neurips.cc/paper/2019/hash/0ec04cb3912c4f08874dd03716f80df1-Abstract.html)
[4]:[Mathieu, Emile, et al. "Continuous hierarchical representations with poincaré variational auto-encoders." Advances in neural information processing systems 32 (2019).](https://proceedings.neurips.cc/paper/2019/hash/0ec04cb3912c4f08874dd03716f80df1-Abstract.html)
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[5]:[Dai, Jindou, et al. "A hyperbolic-to-hyperbolic graph convolutional network." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021.](https://www.computer.org/csdl/proceedings-article/cvpr/2021/450900a154/1yeJgfbgw6Y)
[6]:[Park, Jiwoong, et al. "Unsupervised hyperbolic representation learning via message passing auto-encoders." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021.](https://ieeexplore.ieee.org/document/9577649)
[6]:[Park, Jiwoong, et al. "Unsupervised hyperbolic representation learning via message passing auto-encoders." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021.](https://ieeexplore.ieee.org/document/9577649)
