An Illustrated Guide to the Knowledge Gradient Acquisition Function
We give a short illustrated reference guide to the Knowledge Gradient acquisition function with an implementation from scratch in TensorFlow Probability.
📄 One paper accepted to NeurIPS 2020
Our paper "Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings" was accepted to NeurIPS 2020 as a Spotlight Presentation …
A Handbook for Sparse Variational Gaussian Processes
We summarize the notation, identities, and derivations underlying the sparse variational Gaussian process (SVGP) framework.
Density Ratio Estimation for KL Divergence Minimization between Implicit Distributions
We show how to approximate the KL divergence (in fact, any f-divergence) between implicit distributions using density ratio estimation by probabilistic classification.
Building Probability Distributions with the TensorFlow Probability Bijector API
We illustrate how to build complicated probability distributions in a modular fashion using the Bijector API from TensorFlow Probability.
A Tutorial on Variational Autoencoders with a Concise Keras Implementation
We give an in-depth practical guide to variational autoencoders from a probabilistic perspective.
NumPy mgrid vs. meshgrid
We compare NumPy's `mgrid` and `meshgrid` for building coordinate grids — what each does, why both exist, and how broadcasting often makes them optional.