Papers › Hyperspherical Variational Auto-Encoders
Hyperspherical Variational Auto-Encoders
Tim R. Davidson, Luca Falorsi, Nicola De Cao, Thomas Kipf, Jakub M. Tomczak
The Variational Auto-Encoder (VAE) is one of the most used unsupervised machine learning models. But although the default choice of a Gaussian distribution for both the prior and posterior represents a mathematically convenient distribution often leading to competitive results, we show that this parameterization fails to model data with a latent hyperspherical structure. To address this issue we propose using a von Mises-Fisher (vMF) distribution instead, leading to a hyperspherical latent space. Through a series of experiments we show how such a hyperspherical VAE, or 𝒮-VAE, is more suitable for capturing data with a hyperspherical latent structure, while outperforming a normal, 𝒩-VAE, in low dimensions on other data types. Code at http://github.com/nicola-decao/s-vae-tf and https://github.com/nicola-decao/s-vae-pytorch
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Link Prediction | Citeseer | S-VGAE | AP | 95.2 | #7 of 13 | Archive leaderboard | report |
| Link Prediction | Citeseer | S-VGAE | AUC | 94.7 | #7 of 13 | Archive leaderboard | report |
| Link Prediction | Cora | S-VGAE | AP | 94.1% | #6 of 13 | Archive leaderboard | report |
| Link Prediction | Cora | S-VGAE | AUC | 94.1% | #6 of 13 | Archive leaderboard | report |
| Link Prediction | Pubmed | S-VGAE | AP | 96.0% | #8 of 13 | Archive leaderboard | report |
| Link Prediction | Pubmed | S-VGAE | AUC | 96.0% | #8 of 13 | Archive leaderboard | report |
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