text: 387,67,981443382399643653,50343115,Thomas Kipf,"New paper on learning hyperspherical latent spaces: Hyperspherical Variational Auto-Encoders (with @im_td, L. Falorsi, @nicola_decao, @jmtomczak). Useful trick for learning node embeddings in graphs and for semi-supervised learning. <LINK> <LINK> @riceasphait @im_td @nicola_decao @jmtomczak Thanks! All credit goes to @im_td, Luca Falorsi & @nicola_decao - and to @jmtomczak for co-supervising. The paper is currently under review at UAI. @riceasphait @im_td @nicola_decao @jmtomczak Thanks! All the best for your submission as well :-)",https://arxiv.org/abs/1804.00891,"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 $\mathcal{S}$-VAE, is more suitable for capturing data with a hyperspherical latent structure, while outperforming a normal, $\mathcal{N}$-VAE, in low dimensions on other data types. ",Hyperspherical Variational Auto-Encoders,3,"['New paper on learning hyperspherical latent spaces: Hyperspherical Variational Auto-Encoders (with @im_td, L. Falorsi, @nicola_decao, @jmtomczak). Useful trick for learning node embeddings in graphs and for semi-supervised learning. <LINK> <LINK>', '@riceasphait @im_td @nicola_decao @jmtomczak Thanks! All credit goes to @im_td, Luca Falorsi &amp; @nicola_decao - and to @jmtomczak for co-supervising. The paper is currently under review at UAI.', '@riceasphait @im_td @nicola_decao @jmtomczak Thanks! All the best for your submission as well :-)']",18,04,537