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.

© Louis Tiao 2021

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