text:   Variational Autoencoders",5,"['Excited to share our #AISTATS2020 paper with @cwcyau, where we propose the BasisVAE: a VAE framework for\xa0clustering features (allowing for scale and/or translation invariance).\xa0\n\nPaper: <LINK>\n\nPyTorch implementation: <LINK> <LINK>', 'Our goal is to have a joint model for \n1) dimensionality reduction (inferring latent z) and \n2) clustering features. \nSpecifically, based on the mappings z -&gt; data, we want to find features that have a similar shape (scale invariance) and those that are shifted (translation inv). https://t.co/zrHD7O49T4', 'We propose to achieve this within the VAE framework. Specifically, we modify the decoder network by embedding a probabilistic clustering prior within the last layer of the decoder. This lets us learn a one-hot basis function representation as part of the decoder network. https://t.co/p7Juamjh4S', 'Training illustrated on a toy example: (A) scale-invariant BasisVAE on the left, and (B) scale-and-translation-invariant on the right. https://t.co/U8hUtJIf2c', 'Due to the scalability of VAEs, we can use this on large-scale data sets, e.g. single-cell gene expression. On a mouse spermatogenesis data set (Ernst et al 2019) we show how BasisVAE lets us jointly learn a one-dimensional representation and cluster genes with similar shapes. https://t.co/75hUQVOqwk']",20,03,1223