A summary of notation, identities and derivations for the sparse variational Gaussian process (SVGP) framework.
This post demonstrates how to approximate the KL divergence (in fact, any f-divergence) between implicit distributions, using density ratio estimation by probabilistic classification.
We illustrate how to build complicated probability distributions in a modular fashion using the Bijector API from TensorFlow Probability.
We formalize the problem of learning interdomain correspondences in the absence of paired data as Bayesian inference in a latent variable model (LVM), where one seeks the underlying hidden representations of entities from one domain as entities from …
Expanding the scope and applicability of variational inference to encompass implicit probabilistic models.
Aboleth is a minimalistic TensorFlow framework for scalable Bayesian deep learning and Gaussian process approximation.
Determinant is a software service that makes predictions from sparse data, and learns what data it needs to optimise its performance.
Revrand is a full-featured Python library for Bayesian generalized linear models, with random basis kernels for large-scale Gaussian process approximations.
An in-depth practical guide to variational encoders from a probabilistic perspective.