Corpus ID: 202677218

Scalable Deep Unsupervised Clustering with Concrete GMVAEs

@article{Collier2019ScalableDU,
  title={Scalable Deep Unsupervised Clustering with Concrete GMVAEs},
  author={Mark Collier and Hector Urdiales},
  journal={ArXiv},
  year={2019},
  volume={abs/1909.08994}
}
Discrete random variables are natural components of probabilistic clustering models. A number of VAE variants with discrete latent variables have been developed. Training such methods requires marginalizing over the discrete latent variables, causing training time complexity to be linear in the number clusters. By applying a continuous relaxation to the discrete variables in these methods we can achieve a reduction in the training time complexity to be constant in the number of clusters used… Expand
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