Fully Bayesian autoencoders with latent sparse Gaussian processes

Tran, Ba-Hien; Shahbaba, Babak; Mandt, Stephan; Filippone, Maurizio
ICML 2023, 40th International Conference on Machine Learning, 23-29 July 2023, Honolulu, Hawaii, USA / PMLR 202:34409-34430

Autoencoders and their variants are among the most widely used models in representation learning and generative modeling. However, autoencoder-based models usually assume that the learned representations are i.i.d. and fail to capture the correlations between the data samples. To address this issue, we propose a novel Sparse Gaussian Process Bayesian Autoencoder (SGPBAE) model in which we impose fully Bayesian sparse Gaussian Process priors on the latent space of a Bayesian Autoencoder. We perform posterior estimation for this model via stochastic gradient Hamiltonian Monte Carlo. We evaluate our approach qualitatively and quantitatively on a wide range of representation learning and generative modeling tasks and show that our approach consistently outperforms multiple alternatives relying on Variational Autoencoders.

Poster / Demo
Data Science
Eurecom Ref:
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