In this paper, we address the problem in a completely different way by considering a random inference model, where we model the mean and variance functions of ...

In particular, our model can quantify the difficulty in posterior approximation by a Gaussian variational density. Inference in our GP model is done by a single ...

Jul 20, 2022 · In this blog we presented our recent work on Gaussian process inference error modeling for VAE. Our approach significantly reduces the posterior ...

In this paper, we address the problem in a completely different way by considering a random inference model, where we model the mean and variance functions of ...

In this paper, we address the problem in a completely different way by considering a random inference model, where we model the mean and variance functions of ...

The semi-supervised Gaussian processes model has an explicitly probabilistic interpretation, and can model the uncertainty among the data and solve the complex ...

In this work, we introduce the Gaussian Process Prior Variational Autoencoder (GPPVAE), an extension of the VAE latent variable model where correlation between ...

Blog(2). [CVPR 2022 Series #6] Gaussian Process Modeling of Approximate Inference Errors for Variational Autoencoders · Variational Autoencoder (VAE) [5,6] ...

Gaussian Process Modeling of Approximate Inference Errors for Variational Autoencoders · Minyoung Kim. Computer Science. Computer Vision and Pattern Recognition.

Conventional variational autoencoders fail in modeling correlations between data points due to their use of factorized priors. Amor- tized Gaussian process ...