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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 Bayesi...
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... begin our empirical evaluation by considering the moving ball dataset proposed by Pearce (2019 Fig. 2 illustrates the performance of the considered methods in terms of root mean squared error (RMSE). The results show that our GP-BAE model performs much better than GP-VAE (Pearce, 2019) though both models use the same full GP priors. In addition, by treating inducing inputs and kernel hyper-parameters of sparse GPs in a Bayesian ...
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