Model Selection for Bayesian AutoencodersDownload PDF

May 21, 2021 (edited Nov 05, 2021)NeurIPS 2021 PosterReaders: Everyone
  • Keywords: Bayesian methods, Probabilistic models, Model selection, Bayesian Autoencoders
  • TL;DR: We develop a novel method for carrying out model selection for Bayesian autoencoders by means of prior hyper-parameter optimization.
  • Abstract: We develop a novel method for carrying out model selection for Bayesian autoencoders (BAEs) by means of prior hyper-parameter optimization. Inspired by the common practice of type-II maximum likelihood optimization and its equivalence to Kullback-Leibler divergence minimization, we propose to optimize the distributional sliced-Wasserstein distance (DSWD) between the output of the autoencoder and the empirical data distribution. The advantages of this formulation are that we can estimate the DSWD based on samples and handle high-dimensional problems. We carry out posterior estimation of the BAE parameters via stochastic gradient Hamiltonian Monte Carlo and turn our BAE into a generative model by fitting a flexible Dirichlet mixture model in the latent space. Thanks to this approach, we obtain a powerful alternative to variational autoencoders, which are the preferred choice in modern application of autoencoders for representation learning with uncertainty. We evaluate our approach qualitatively and quantitatively using a vast experimental campaign on a number of unsupervised learning tasks and show that, in small-data regimes where priors matter, our approach provides state-of-the-art results, outperforming multiple competitive baselines.
  • Supplementary Material: pdf
  • Code Of Conduct: I certify that all co-authors of this work have read and commit to adhering to the NeurIPS Statement on Ethics, Fairness, Inclusivity, and Code of Conduct.
18 Replies

Loading